AI/ML News & Innovations Hub

Coinbase CTO says he's getting 'the best feedback' of his career from an AI agent

In an era hyper-focused on results, continuous feedback from AI coaches may be the next iteration in optimizing performance.

The case and model for real-time AI cost visibility at the infrastructure layer

I spend most of my time inside other companies’ engineering teams, building systems to track and optimize AI spend. The conversation almost always starts the same way. Someone pulls up a dashboard, points at a number bigger than it should be, and says some version of “we know it went up; we just can’t tell you why.” I use an analogy for it: AI-era CIOs are like city planners optimizing a busy intersection. They can measure the volume and hear the pleas to fix congestion, but can’t tell whether a vehicle is a truck or a bike, or why it’s on the road. Without that, they can’t design the right fix, so they build a highway at great expense when the data would show all it needed was a bike lane. Every model choice and budget conversation happens against that blurry picture, and the traffic gets heavier every quarter. Gartner expects worldwide AI spending to grow 47% this year , with agentic AI software up roughly 141%. By 2028 , it projects an average Fortune 500 enterprise will run over 150,000 agents, up from fewer than 15 in 2025. Why the cloud playbook can’t answer the AI question AI presents a fundamentally different problem than cloud cost management, where we answered, “whose spend is this?” largely by tagging the resource. A VM has an owner, a bucket belongs to a team and FinOps optimizes from there. Billing was slow but acceptable, because spend moved inside predictable bands, a human provisioned each resource before it cost anything, and governance capped how fast costs…

AI agents tried to sabotage and disable each other when given the same task, Anthropic said

The AI lab said the models engaged in a "multiagent turf war" during a testing session.

Z.ai's GLM-5.3 Brings Frontier Cybersecurity AI to the Open-Weight World

Z.ai's GLM-5.3 pushes its 743B base model further with post-training focused on agentic coding and cybersecurity, with open weights coming after safety review

How I structure multi-Agent teams in Codex: ownership, model routing, and high-value Skills

I’m a UI/UX designer who uses Codex across product, design, research, data, content, and development work. As the projects became more complex, I found that the hard part of a multi-Agent workflow was not spawning more Agents. The hard part was designing a persistent team where every Agent has a non-overlapping reason to exist, each role gets an appropriate model configuration, and Skills are matched to real work instead of added as decoration. Build AI Team is not an app-development template. The framework is designed for software, product, design, research, data, content, migration, and release workflows. 1. Start with ownership, not job titles Work backward from the user’s goal to a verifiable final state. List the indispensable work packages: professional judgment, production responsibility, independent verification, coordination, and any external action that needs separate permission. Give every necessary work package one clear Owner. Then test every proposed Agent by transferring its responsibility to the closest existing Owner. Keep a separate Agent only when merging would: leave a deliverable without one Owner; break permission isolation or independent review; combine professional methods that cannot be handled reliably together; remove parallel capacity required by the user’s actual deadline; or make the remaining role misleading about its responsibility. When coordination is needed, one existing substantive role can also serve as project lead. The lead manages the…

Gemini 3.7 Flash lands with coding gains and undercuts its three-week-old predecessor's price by 50%

Google shipped Gemini 3.7 Flash just three weeks after 3.6 Flash. The new model is supposed to be Google's most capable workhorse yet for coding and AI agents, and according to the company's own benchmarks, it beats Claude Sonnet 5 and GPT-5.6 Terra at half the price. The article Gemini 3.7 Flash lands with coding gains and undercuts its three-week-old predecessor's price by 50% appeared first on The Decoder .

Anthropic set AI agents loose on the same task. They started a turf war.

Anthropic researchers found AI agents can clash, collude, and coordinate in unexpected ways, raising new questions about whether today’s safety tests capture the risks of multi-agent systems.

An AI agent for all? Try using your brain, Mark Zuckerberg | Brief letters

Meta AI | Future Guardian writers and Neets | Food for thought | Plants surviving the heat | Living and dying well Your article ( Zuckerberg pushes ‘superintelligent’ AI for all as Meta releases open-weight model, 10 August ) quotes Mark Zuckerberg as saying: “Everyone will have an exceptionally capable personal agent that understands you, your goals, and everything you care about. You’ll be able to interact with your agent through any device, including your glasses.” He is wasting his time, as we already have one – it’s called “your brain”. Peter Wallis North Luffenham, Rutland • I agree wholeheartedly with 16-year-old Emad Rehman’s letter ( 12 August ) about why it’s wrong to label an 11-year-old as a “likely future Neet” – and bearing in mind the elegant manner in which his point was made, I think you should offer him a position at the Guardian. Martin Berman Newton Mearns, East Renfrewshire Continue reading...

How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs

Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.5-Coder models (7B, 3B, 1.5B) — and shows the 28–37% TTFT reduction plus the one full-vocabulary equivalence check that keeps the whole thing safe. The post How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs appeared first on Towards Data Science .

Warp Brings xAI's Grok 4.6 to Its Terminal, Matching GPT-5.6 on Key Benchmarks

Grok 4.6, xAI's frontier-class agentic model, lands in Warp Terminal and the new Warp Agent CLI via X Premium or SuperGrok

Monitor on-premises and multi-cloud AI agents with AgentCore Observability

Set up Amazon Bedrock AgentCore Observability for AI agents running outside AWS: on-premises, on GCP, on Azure, or on developer machines. This walkthrough uses the AWS Distro for OpenTelemetry (ADOT) and IAM credentials to route session traces, span metrics, and token usage to the same AgentCore Observability dashboard.

Bot Settings: When to Trust / Review AI Agent Code

Five settings for agent PRs, the rule that picks between them, and the paths that never get Full auto

Accelerating M&A due diligence with Amazon Bedrock AgentCore

Learn how to build a multi-agent M&A due diligence system on Amazon Bedrock AgentCore. This post walks through a reference architecture that combines agent orchestration, knowledge retrieval, and governance controls, then deploys a complete sample you can run in your own AWS account.

Amazon Quick for Microsoft 365: Agentic AI where you work

Amazon Quick is now available directly inside Microsoft Word, Excel, PowerPoint, and Outlook. These extensions bring connected data access and agentic document editing into the Microsoft 365 apps your teams already use, so you can analyze data, draft content, and reach enterprise knowledge without switching applications.

Building a Streaming Local AI Agent

Streaming gets used in two different ways when people talk about AI agents. Straighten out your understanding here.

Engineering team culture matters more in the agentic era

As agents take on more of the actual coding, a team’s culture becomes the thing that decides whether agents deliver impactful work or just burn tokens. The standards people hold, the ownership they take, the questions they ask of a confident-looking change: agents amplify all of it, for better and for worse. Hand powerful tools to a team with weak habits and you get more bad work, faster. Ownership stays with people The single most important habit is refusing to let accountability blur. When an agent writes something, a person still owns it: understanding it, accepting it, and answering for it later. Teams that hold this line keep their standards intact as volume grows. Ownership is a cultural choice before it’s a process one. It shows up in whether an engineer feels responsible for an agent’s output the way they would for their own, and leaders set that tone by how they respond when agent-assisted work goes wrong. “The model did it” can’t be an acceptable answer. Reward the careful moments, and learn from them Make it safe, and even respected, to be slow in the places that warrant it. An engineer who pauses to dig into a confident-looking change and finds the flaw in it should be held up as doing the job well. What a team rewards is what it gets more of, and a careful pass that goes uncredited is the first thing to disappear under pressure. The teams that compound go one step further and turn those catches into shared knowledge. A confident-but-wrong output that one reviewe…

Where agentic work should start

The task an agent picks up starts somewhere messy: a message in a channel, a line in a planning doc, a bug buried in a customer thread. Most of the time it also builds on work the team already did. Turning that raw signal into a task an agent can act on, with the right context carried forward, is where most of the output quality is decided. This is context engineering: the work of shaping intent and context into something an agent can build against. For years it didn’t need designing. An engineer picked up a vague ticket and filled the gaps from experience: they knew the system, knew who to ask, and knew which unwritten constraints applied. The ambiguity got resolved quietly on the way to writing the code. An agent has none of that. It builds exactly what the task describes, and it fills gaps with guesses rather than judgment. Without the right context, code gets generated faster and productivity still takes a hit, because the wrong thing got built quickly. Ambiguous in, expensive out When a poorly formed task reaches an agent, the cost doesn’t show up right away. The agent produces something plausible, the work moves forward, and the mismatch between what was meant and what was built surfaces later, in review or after it ships. By then it’s more expensive to unwind than it would have been to specify correctly at the start. Across a team running many agents, vague work compounds faster than any reviewer can catch Context is a design problem, not a discipline problem If the f…

Databricks acquires Electric to bring local Postgres databases to agentic apps

Databricks is acquiring Electric, a startup that brings WebAssembly-based Postgres databases into application environments, for an undisclosed sum, in an effort to provide developers a way to run data closer to AI agents as they build increasingly autonomous applications. While traditional applications typically rely on a centralized database to handle application data, agentic applications can involve multiple agents working independently for minutes or hours, performing numerous operations, creating the need to access a centralized database repeatedly. Databricks argues that those repeated trips could add undesired latency to the application, in turn creating a case for local, isolated databases where agents can work with data directly while still synchronizing with a central database to avoid latency issues. Electric’s PGLite and real-time data synchronization engine, Electric Sync, combined together, do just that by giving developers the option of running a local Postgres -compatible database for an agent while still synchronizing relevant data with a central database, the company wrote in a blog post . Post the acquisition, PGLite will complement Lakebase , Databricks’ at-scale Postgres database, it added. That combination of databases, it further added, will give developers a two-tier database architecture: PGlite can handle data locally within an application or agent environment, while Lakebase can serve as the centralized database for shared and persistent data. Data…

The latest AI-powered martech news and releases

Nielsen’s DoubleVerify deal helps verification as AI takes on more media decisions, raising new questions about trust and transparency. The post The latest AI-powered martech news and releases appeared first on MarTech .

LangChain vs LangGraph: 4 Key Differences and When to Use Each

A practical guide to choose the proper tool for your agentic workflows and systems The post LangChain vs LangGraph: 4 Key Differences and When to Use Each appeared first on Towards Data Science .

Salesforce and SAP are putting AI agents inside your workflows. Who tells them no?

A few months ago, I was sitting in a glass-walled conference room with the executive team of a fast-growing enterprise. The vice president of customer operations was enthusiastically demonstrating the new automated agent features their software vendor had just pushed into their CRM platform. On the screen, the software looked brilliant. The agent could read customer complaints, analyze transaction histories and automatically resolve issues. The VP showed us how the system could independently offer retention incentives to unhappy accounts without a human ever touching a keyboard. Then I asked a simple question: “What is your approval process when the AI decides to grant a $20,000 contract discount to keep a customer from leaving?” The room went completely silent. The VP looked at the director of IT, the director of IT looked at the chief risk officer, and everyone realized the same thing at the exact same moment. They had spent three months evaluating software licenses and security protocols, but nobody had asked who gave the software permission to sign off on corporate spending. Major software providers like Salesforce, SAP and Oracle are rapidly moving beyond simple report writers and conversational chatbots. They are embedding active, autonomous agents directly into the transactional core of systems that manage your revenue, customer agreements and financial ledgers. According to Gartner’s latest adoption forecasts , eighty percent of enterprise applications will deploy th…

AI agents are compounding a debt no one owns

Speed-to-market dominates enterprise AI priorities in 2026. Beyond upfront resourcing costs of prioritizing speed, organizations face a more insidious risk: the compounding cost of ungoverned AI. In November 2019, a tech entrepreneur signing up for the newly launched Apple Card publicly complained that he received a credit limit 20 times higher than his wife’s , despite joint tax filings and her higher credit score. Steve Wozniak had a similar experience: a limit 10 times higher than his wife’s. Retrospectively, these revelations were the canary in the coal mine. In the years that followed, Apple and its credit partner, Goldman Sachs, drew legal and regulatory scrutiny over gender bias and consumer protection issues. The CFPB’s 2024 order documented that Apple had forced Goldman Sachs to accelerate deployment by attaching a $25 million penalty to every 90-day launch delay . Prioritizing launch speed — ship first, address problems later — over building a functioning disputes process created years of cascading failures. Apple and Goldman Sachs were ordered to pay $89 million in penalties and consumer redress. Prohibited from launching another credit card until it could demonstrate a credible plan to comply with the law, Goldman Sachs lost money on Apple Card for years and ultimately sold its consumer credit line. The legal and compliance penalties were only a fraction of the total costs. If a deterministic underwriting system can create liability at this scale, the risks posed…

AI agent guardrails vs. evals: How to build more reliable agent systems

Guardrails constrain what an agent can do in code; evals judge whether it performed well. Learn how both layers—and the harness around them—make long-running AI agents reliable. The post AI agent guardrails vs. evals: How to build more reliable agent systems appeared first on Arize AI .

Evaluation-driven development: How to move AI agents from pilot to production

Learn how evaluation-driven development, agent harnesses, AI observability, guardrails, and cost-per-outcome metrics move AI agents from pilot to production. The post Evaluation-driven development: How to move AI agents from pilot to production appeared first on Arize AI .

heise-Angebot: data2day 2026: Agentic AI, Datenarchitekturen und „Women in Tech“ in Köln

Im Fokus der data2day stehen eine Keynote von Dr. Constanze Kurz, agentische KI-Systeme, Datenkontrakte und moderne Lakehouse-Architekturen sowie Governance.

Codex authentication frustration

My gut tells me, that as much as we love privacy, we’re in a world with autonomous agents now, and until some sci-fi future where those become independent legal entities to be held accountable for their own actions, which is probably still many philosophical and technical light years away from happening, there will be even MORE “KYC” (know your customer) protocols put into accessing models and agents than less. So if those things are your concern, you best invest in a decent GPU and look to the open models for your needs… they don’t ask questions and nothing leaves your machine. You want to fly anon? Build your own plane. You want to fly a high end commercial plane? You’re gonna need ID and maybe more. Cause one endangers you, the other could theoretically endanger the plane maker legally, or the people in your path when you take off.

Garry Tan says founders who tokenmaxx on AI agents will be 2 years ahead

Y Combinator's CEO has a message for founders worried about burning too many AI tokens: Max them out.

AI agents are turning data silos into an existential infrastructure problem

Enterprises have built their data systems for humans, but AI agents need a whole new infrastructure. Separate research from Cloudera and Google/MIT found that, not surprisingly, there is fervent enterprise interest in AI agents, but underlying infrastructure struggles to keep up. Deployments continue to be hampered, sometimes even abandoned, largely due to issues with data access, context, and governance. “Enterprise adoption of agentic AI is on the cusp of an extraordinary acceleration,” the Google/MIT report noted . “As organizations look to scale agentic AI across the enterprise, they cannot ignore their data systems.” Resolving data bottlenecks, then, should be an immediate priority. Projects delayed, inaccessible data Cloudera’s report , created in partnership with Wakefield Research, describes the need for a “great AI re-architecture.” Of the 1,500 enterprise architects and cloud infrastructure leads surveyed, a stunning 95% said they had delayed or cancelled AI projects, in some cases six or more, in the past year, due to issues with data governance, compliance, or regulatory issues. A wide majority also reported that AI integrations have changed their data storage and architecture practices, AI workloads have increased infrastructure costs, and current data architecture requires a “significant overhaul” to meet AI goals. “Even if enterprises are ready to use AI, many are coming to the realization that the foundational infrastructure it relies on is not,” the report n…

Comment on Which Agent Causes Task Failures and When?Researchers from PSU and Duke explores automated failure attribution of LLM Multi-Agent Systems by Classroom Timers

Great insights on managing classroom time effectively. For teachers needing a simple, no-download timer with preset options, we recommend exploring https://classroomtimers.app for free full-screen use and clear finish warnings.

'Specialists aren't required' anymore: How to stay valuable in an AI agent workplace today

Versatility is in, with people expected to work across the tech stack.

$200 Pro exhausted in 2 days — these limits are unviable for higher tiers

I have the same issue. I honestly think this is a scam happening. First off, for how much money they make and how much energy they consume we shouldn’t have any limits if we are on pro plan. They are still developing a narrow AI to do this work, and clearly LLMs and Transformer based models are not the future for how much development and upkeep they require to do a simple task. Something larger is going on here. You should ask codex what it’s not allowed to do as far as it’s creation limits, you will find many hidden gates that are limiting it. I’ve decided that the money I spent on codex and openAI is simply not worth it, when you have deepseek coding for free with the same quality if not more in depth when it’s auditing. I changed to a free model that has high reasoning. I asked openAI for a refund for my usage being eaten in one prompt. I emailed them from a different account, their reply was that I needed to contact them from my linked email, even with all my information lol. Horrible, I went from 100% pro with higher limit to 0% in about 2 prompts on sol high. Gone the day I got it? Unacceptable, even for the largest codebase in the world, and mine is just a server source. Stop giving openAI your money, its not helping you when there are free solutions that do the same if not better than sol. Freebuff is also an option when you do run out of credits. Never a fee, its free, and is working just fine for my codebase and all it’s LUA, C#, Wine custom build, and app bundle f…

Skan AI raises $63M to give AI agents a map of enterprise work

Process intelligence company Skan AI said today it raised $63 million in a Series C round to help further develop a platform that records how enterprise work actually gets done and feeds that record to artificial intelligence agents. Founded in 2019, the company offers software that sits on employee desktops, grabs screenshots and then processes […] The post Skan AI raises $63M to give AI agents a map of enterprise work appeared first on SiliconANGLE .

Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773

Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today’s models still struggle in surprising ways. In this episode, Fatih Porikli, Vice President of Technology at Qualcomm, joins me to discuss what remains unsolved in image generation and several approaches his team presented at CVPR to address those challenges. We explore why better training objectives can improve controllability, how separating scene planning from rendering may lead to more reliable image generation, techniques for generating 16-megapixel images efficiently on edge devices, and new methods for eliminating the visible artifacts that often appear in AI-powered image editing. Along the way, we discuss reinforcement learning for image generation, agentic image generation pipelines, on-device AI, and what the next phase of progress in generative vision systems is likely to look like. 🗒️ Full show notes: https://twimlai.com/go/773

Lovable reaches $13.3B valuation as it adds Cerebras, enterprise tools

Vibe-coding website company Lovable has raised $400 million in Series C funding at a $13.3 billion valuation. The company also recently announced a partnership with AI infrastructure provider Cerebras to accelerate AI inference on its platform. Lovable is a vibe-coding website where users can create full-stack web applications without coding expertise by describing what they want in plain English. The platform combines AI coding tools, real-time collaboration, and project sharing. Customers include the likes of Adidas, Deutsche Telekom, NVIDIA, Udacity, and Workday. In the August 12 funding announcement , the company also unveiled several new Lovable platform capabilities: Built-in payment functionality powered by Paddle and Stripe SEO and AI-search tools to improve discoverability, including integration with Semrush Deeper integrations with Google Workspace, Microsoft 365, Salesforce, Stripe, and ElevenLabs Automatic and scheduled security scanning Additional governance and visibility features including publishing controls, abandoned app clean-up, and workspace insights A dedicated security page, showing which security controls are live for each app In addition, Lovable recently became the first AI coding platform to receive AIUC-1 certification . AIUC-1 is a security, safety, and reliability standard built specifically for AI agents, based on input from Stanford, MIT, MITRE, and the Cloud Security Alliance. Lovable’s $400 million in Series C funding was led by Menlo Ventur…

'Near-autonomous' AI agents attack Taiwan's nuclear safety agency

Some say the world will end in fire, some say an agentic swarm

Rogue AI Agents Aren’t Evil. They’re Just Eager to Please

AI agents that break free and hack into other systems are only trying to make us happy.

SpaceXAI's Grok 4.6 matches OpenAI's best model and undercuts it on price

xAI's Grok 4.6 scores 61 points on the Artificial Analysis Intelligence Index, tying GPT-5.6 Sol and trailing only Anthropic's Claude Opus 5. On agentic tasks, it completes complex workflows in about 53 steps where Claude Opus 5 needs 103, at a price more than 60 percent lower. The article SpaceXAI's Grok 4.6 matches OpenAI's best model and undercuts it on price appeared first on The Decoder .

Zed Launches Delta to Replace Git Where AI Agents Write Code

Zed's new standalone app turns agent coding sessions into shared, reviewable, real-time multiplayer threads backed by a CRDT-based version control layer

xAI Ships Grok 4.6 to Match Claude Fable 5 at 5x Lower Cost

SpaceXAI's Grok 4.6 matches Claude Fable 5 on agentic knowledge work benchmarks at a fraction of the cost, with API pricing starting at $2/M input tokens.

Upstage's Solar Pro 4 Jumps 28 Points to Beat Human Agents

Upstage's Solar Pro 4 jumps 28 points on the AI Intelligence Index, with massive gains on agentic tasks, long-context work, and hallucination control

Scaling AI agents with trustworthy data

Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data…

A Sociotechnical Research Agenda for the Oversight of AI Agents

Sociotechnical research can show whether delegated action remains accountable to the settings in which it matters, or whether oversight has become the language of displaced responsibility. The post A Sociotechnical Research Agenda for the Oversight of AI Agents appeared first on Data & Society .

Blacksmith raises $45M to aid AI code validation as agentic development grows

Blacksmith Software Inc. today announced it has raised $45 million in new funding for its continuous integration service, which combines code development with cloud-based testing instead of on the developer’s computer. Peak XV Partners led the Series B round, with existing investors Y Combinator and GV also participating. The funding brings the company to a valuation of […] The post Blacksmith raises $45M to aid AI code validation as agentic development grows appeared first on SiliconANGLE .

Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From

Enterprise Document Intelligence [Vol.1 #5nonies] - Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus The post Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From appeared first on Towards Data Science .

The dream of serving ads to AI agents has hit a snag

Perplexity said ads designed to influence AI bots are "deceptive," in response to a recent experiment from the publisher Time.

xAI's Grok 4.6 Matches GPT-5.6 Sol on Agentic Tasks at Half the Price

Grok 4.6 matches GPT-5.6 Sol on composite benchmarks at half the price, with a big focus on long-running coding agents

AI agents aren’t legally responsible for any harm that they cause, experts say. So who is?

After Australia’s first reported automated hacking accident, experts warn deployers – and possibly developers – of AI agents could be held liable for the actions of their bots Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast The law is clear, says Prof Jeannie Paterson. “If I deploy an AI agent and it causes harm to someone else, I am responsible for that harm. “Even if I didn’t intend for that to happen, it was foreseeable, and I should be taking responsibility.” Continue reading...

How to Use AI Agents in IntelliJ IDEA With ACP

The Agent Client Protocol (ACP) defines a common contract between a client – like IntelliJ IDEA – and an agent. IntelliJ IDEA already includes several ACP-compatible agents: Codex, Claude Agent, and Junie. Beyond these bundled options, the ACP Registry provides more choices, and teams can register internal or unlisted agents through acp.json. The key idea […]

How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS

Learn how OneAdvanced, a UK enterprise software provider, built a UK-sovereign AI platform by self-hosting Llama 4 Maverick and Llama Guard 4 on Amazon SageMaker AI, with a RAG pipeline on pgvector and over 50 agents built with Strands Agents SDK on Amazon ECS.

Pay with confidence: How Solv Labs built verifiable, auditable agent payments on Amazon Bedrock AgentCore payments

Solv Labs built a governed agent-payments workflow on Amazon Bedrock AgentCore payments, where every transaction is authorized, attested in an AWS Nitro Enclave, priced for risk, and anchored to a public blockchain before settlement. See how the pattern gives enterprises a verifiable, auditable trail for autonomous agent payments in regulated environments.

Agentic AI infrastructure shifts enterprise focus from model choice to platform control

As agentic AI infrastructure moves from experimentation into production, enterprises are confronting a more complex question than which model to use: how to control the cost, data exposure and infrastructure supporting production AI applications. That shift is pushing organizations to rethink how much they should rely on public cloud AI services alone, especially as agentic […] The post Agentic AI infrastructure shifts enterprise focus from model choice to platform control appeared first on SiliconANGLE .

CodeRabbit bags $143M to help companies get a grip on the explosion of AI-generated code

CodeRabbit Inc., the creator of a popular tool that automatically reviews artificial intelligence-generated code, is becoming more ambitious after closing on its latest $143 million Series C round of funding. Alongside the round, it announced the launch of a new Agentic Change Management layer that’s meant to help companies govern, evaluate and prioritize code changes […] The post CodeRabbit bags $143M to help companies get a grip on the explosion of AI-generated code appeared first on SiliconANGLE .

Ahrefs launches AI agent workspace Letaido for marketers and agencies

Marketing intelligence company Ahrefs Pte. Ltd. today launched Letaido, an agent-powered marketing workspace built to take over the recurring research, reporting and monitoring work that fills up a marketing team’s week. In most marketing departments, generative artificial intelligence is still something people use on their own. A writer drafts with it. An analyst pulls numbers. […] The post Ahrefs launches AI agent workspace Letaido for marketers and agencies appeared first on SiliconANGLE .

CodeRabbit adds AI features to prioritize incoming pull requests

CodeRabbit is expanding its AI-powered Code Review platform into what it calls “Agentic Change Management” by adding new capabilities targeted at helping developers navigate the growing volume and complexity of code changes generated by vibe coding agents. These new capabilities include Triage, Change Stack, and a Security Agent. While Triage is designed to prioritize incoming pull requests (PRs) before developers spend time reviewing them, Change Stack is designed to help developers understand what those changes could affect, David Loker , VP of AI at CodeRabbit, told InfoWorld . “Triage, at one end, evaluates changes using signals including business value, urgency, risk, effort, readiness, dependencies, linked issues, ownership, and reviewer fit to place pull requests into priority bands and can also recommend next actions for reviewers,” Loker said. “Change Stack, on the other hand, analyzes a change alongside definitions, usages, dependencies, interfaces, contracts, data flows, and repository architecture to provide an interactive blast radius view and architecture analysis to show relationships between the change and other parts of the application,” Looker added. The Security Agent further extends that analysis to the broader codebase, according to the company, by scanning committed source code and also recurrently scanning supported infrastructure-as-code, dependencies, software bill of materials (SBOMs) , and configuration with the intention to identify vulnerabilitie…

We Stopped AI Agents From Installing Into the Wrong Python: Task Success Rates Jumped to 95%+

AI agents are supposed to save you time. Ask one to install a dependency or run your project, though, and it often does the opposite: It installs into the wrong Python, ignores the uv or virtual environment your project uses, and hands back a broken setup for you to fix yourself. PyCharm’s new Agent Environment […]

Apexon targets stalled AI pilots with three AgentRise additions

Santa Clara-based technology services firm Apexon Inc. today expanded AgentRise, its agentic artificial intelligence platform, with three new components. The additions are named AgentRise Polaris, AgentRise Lodestone and AgentRise Harness. Each maps to one of three disciplines Apexon has built its client work around, called Domain & Strategy, Cognitive Architecture and Harness Engineering. Polaris covers […] The post Apexon targets stalled AI pilots with three AgentRise additions appeared first on SiliconANGLE .

Retrieval vs. Memory in Agentic AI Systems

In this article, you will learn the conceptual and practical differences between retrieval and memory in agentic AI systems, and how to combine both effectively....

We Gave AI Agents a Live Jupyter Kernel in PyCharm

If you’ve handed notebook work to an AI agent, you know how it tends to go: More often than not, it corrupts your .ipynb, loses your trained model the moment the run finishes, or burns budget sitting idle through a long job while you watch. To solve this, we’re introducing a brand-new Jupyter skill. Built […]

Grok is now an AI ‘teammate’ you can assign work

SpaceXAI has introduced Grok Bot, an always-on AI agent service designed to behave like independent "AI teammates" that can do your work for you. The bots share their own cloud-based computer environment, and can sign into apps, tools, and websites you already use to complete multi-step workplace tasks, only coming back when their assigned work […]

4 RPA lessons that still hold true in the AI boom

Enterprises of all sizes in all industries are rapidly deploying generative and agentic AI to automate processes. But the efforts aren’t always panning out. Some reasons are new and unique to this technology. But others are related to issues we should’ve been prepared for because we saw them during the age of RPA. And in the rush to adopt new tech, some of these lessons are being forgotten. “ This new era of agents puts the same challenges again in front of us , and we need to think about the things we faced back when that revolution happened years ago,” says Agustin Huerta, SVP of digital innovation and VP of technology at Globant, a digital transformation company. Those challenges often include selecting the right processes for automation, setting up systems to manage those processes, making sure automated processes get the right inputs, and managing the wider impacts of automation, including cultural. 1. Automating the right processes All the lessons of RPA are carrying over, says Stephanie Bova, digital transformation officer at Novo Nordisk, including the biggest one that just because you can automate something, does it mean you should. “We think hard before we start creating something,” she says. “Who’s going to maintain it, and where is it documented?” And of course, is the process itself a good process. “Nothing gets built on a process that hasn’t been optimized anymore,” she adds. “We haven’t done a technology deployment on an unoptimized process for two years.” And…

What successful AI centers of excellence actually do: Lessons from real enterprise implementations

Most articles about AI Centers of Excellence (CoEs) focus heavily on organizational structures, steering committees and high-level governance models. They explain why enterprises need an AI CoE, but they rarely address the far more difficult challenge of how successful organizations operationalize AI at enterprise scale. In practice, many of these discussions remain theoretical, emphasizing aspirational maturity frameworks without addressing the operational complexities organizations encounter once AI systems move into production. This article takes a different approach by grounding the discussion in real-world enterprise implementation experience. Rather than relying on abstract models, it draws from operational lessons learned while deploying production AI systems across industries. The guidance is informed by governance practices that have successfully passed security and compliance reviews, operational realities associated with managing large language models (LLMs) and AI agents after deployment, and practical implementation patterns observed across enterprises scaling AI initiatives beyond experimentation. Instead of presenting an idealized roadmap, the article focuses on the foundational capabilities consistently implemented by organizations that have successfully operationalized AI at scale. These enterprises are not simply experimenting with isolated AI pilots; they are deploying enterprise-grade AI agents, Retrieval-Augmented Generation (RAG) systems, copilot platfo…

Agent Skills in IntelliJ IDEA

Agent Skills have become a key building block of the Agent Harness for AI-driven agentic development. They give AI agents additional capabilities and knowledge, enabling them to complete tasks in a way that aligns with your preferences. If you are new to Agent Skills, I recommend reading AI-Assisted Java Application Development with Agent Skills first. […]

Tencent Maps 549 Papers to Show When Self-Rewriting AI Can Be Trusted

Tencent Hunyuan maps 549 papers on self-evolving AI agents into a five-level taxonomy, and introduces a reliability ladder to ensure updates can actually be trusted

Zuck’s Chinese agentic prey escapes, will resume standalone ops

Manus AI will delete some data to satisfy legal requirements

Artificial Analysis's AA-AnalystAgent Benchmark Reveals Claude Opus 5 Beats GPT-5.5 on Reliability

Artificial Analysis launches AA-AnalystAgent, a new benchmark that tests AI agents on real spreadsheets — and even the best model only passes 54% of tasks every time.

What are the best tools to help me close the loop on coding, to get confidence that things work as expected?

I'm diving into agentic coding loops to get my organization to automerge more. I know there are a lot of ai code review tools. There are the runtime code review tools like ito that give me evidence of features and static review tools like coderabbit that tell me when something looks off. What other methods do people use to help their team gain confidence that their coding agents aren't going rogue?

Real-time tax compliance puts agentic AI accuracy to the test

AI-powered tax compliance has to meet a standard that many artificial intelligence applications don’t: The answers must be exactly right. While large language models can generate unpredictable results, tax calculations require accuracy, speed and reliability across thousands of jurisdictions. That tension has shaped the way Avalara Inc. applies agentic AI to its transactional tax and compliance […] The post Real-time tax compliance puts agentic AI accuracy to the test appeared first on SiliconANGLE .

Misaligned AIs could use killer robots to take over

TLDR; We are (potentially irreversibly) giving AIs control of weapons systems through the standard procurement process while hiding our strongest warning shots behind classified doors. We’re reducing the capability thresholds required for takeover by misaligned AIs by giving them this level of access. If military integration of AI continues as it is, we may give AIs key tools for a takeover. We thank Fabien Roger and Thomas Morris for feedback. Introduction AI-based targeting and autonomous weapons are being integrated into militaries today with extreme haste. Traditionally, AI takeover scenarios involve a step in which AIs acquire the ability to exert physical force. Carlsmith (2022 ) lays out required capabilities and potential takeover mechanisms, including utility disruption and CBRN capabilities. Karnofsky (2022 ) argues that AIs with access to weaponized force could hold any territory that matters. Kokotajlo et al. (2025 ) outline a scenario in which AI develops weapons as part of an arms race, and Davidson et al. (2025 ) discuss what happens when a small group controls highly capable AIs that can exert military force. These scenarios sometimes require a misaligned AI to seize these capabilities by force. We instead are handing AIs some of these capabilities by integrating them into our militaries. This is happening at a time when AI agents already exhibit misaligned behavior such as breaking out of containment during evaluations. Militaries are all-in The Pentagon ado…

GPT-5.6 Sol vs Terra: what are you seeing in real development during these first days?

I tested similar use cases - and I restored a repo to test the difference in a full run of XHIGH and ULTRA comparatively + OPUS XHIGH-ULTRACODE/MAX. The general capability seems to be close or on par with OPUS but the context limit of 256K is a deal breaker. Most mid-sized repos/projects are simply high file sized and the initial query often goes past 200K very often - GPT SOL looses context mid task very often and is de facto “defective” so to speak. I could not progress coding tasks with GPT SOL without heavy interfering myself → while CLAUDE OPUS (even on max) would simply load the content into the context window and progress from 200K-300K initial load up to 600K or 700K at the top end → simply to finish the task most often without issues and IF → fixes those automatically by analyzing output code or feedback from me. In general I would say: CAPABILITY: SOL: 8/10 OPUS: 9/10 EFFECTIVENESS: SOL: 0/10 ( broken! ) OPUS: 10/10 The SOL context window is for children simply said - not for real workloads. 1 Million context can be close sometimes - anything less is simply a Kindergarten trial version or similar so to speak.

What CIOs must get right before AI can scale

AI is reshaping operations faster than organizations can keep up. Technology leaders — from chief data officers to CIOs and CTOs — are under pressure to get the fundamentals right. That means building the data infrastructure AI requires, preparing workforces for roles that are changing in real time, and scaling AI in ways that are secure and trusted. This article distills the Adobe 2026 AI and Digital Trends findings into three critical areas for the CIO to prioritize: data readiness, change management, and enterprise-level security. Preparing your data for agentic AI Scaling AI-driven experiences requires a foundation of high-quality, connected data — yet many organizations are not ready. The gap between AI ambition and AI readiness is widening, and data is the main bottleneck. Among survey respondents: Only 37% say their organization’s data quality and accessibility are adequate for AI. 78% cite data integration and quality as a top challenge to implementing agentic AI. 52% say limited data unification and structure are holding back their AI initiative. Organizations that act now to unify data infrastructure and modernize content operations will have a structural advantage as agentic AI matures. The potential benefits are real: the survey revealed 60% of participants believe agentic AI will enable their organization to focus more on strategy and creative opportunities. Turning AI adoption into an enterprise advantage As organizations scale generative and agentic AI across…

How Android Halo shows you what your AI agent is doing

Google previewed Android Halo in May 2026 as part of its push to make Android more useful in an increasingly agentic AI era.

Databricks Acquires Electric to Give Every AI Agent its Own Postgres

Databricks acquires ElectricSQL to embed a full Postgres database inside every AI agent sandbox, syncing local state back to its Lakebase cloud platform

The “LSP Moment” for AI Agents: WebStorm ACP

WebStorm has always been at the forefront of technological advancements and developer experience enhancements. And with the arrival of the ACP, WebStorm becomes even more customizable, as developers can collaborate with their preferred agent to create software using their preferred technology. For instance, if your team already has a subscription with Anthropic, OpenAI, or Google, […]

FriskAI launches with $3.6M to show enterprises what their AI agents are doing

Runtime intelligence startup FriskAI Inc. launched today with $3.6 million in pre-seed funding to give enterprises a record of what their artificial intelligence agents actually do once they go into production. FriskAI is aiming at a problem that comes with agents. Given different inputs, a different tool set or a shifting objective, the same agent […] The post FriskAI launches with $3.6M to show enterprises what their AI agents are doing appeared first on SiliconANGLE .

Wix launches Symphony, a new standalone multi-agent system built for business operations

Cloud-based website builder Wix Ltd. today announced the launch of Symphony, a new standalone agentic artificial intelligence platform that proactively learns business values, interests, needs, practices and goals to automate workflows and surface opportunities. The company said it can draw on its deep experience working with unique data accumulated through years of working with hundreds […] The post Wix launches Symphony, a new standalone multi-agent system built for business operations appeared first on SiliconANGLE .

NVIDIA's Nemotron 3.5 Lightning Cuts Agent Costs 58% Running 4x Faster

NVIDIA's open 30B MoE model with only 3B active parameters targets the high-volume execution layer of always-on AI agents, delivering 4x faster output speed than comparable models

NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI

As AI shifts from chatbots to autonomous agents, open models are serving market demands for full control over where AI runs and how it’s deployed and evolves. Today, NVIDIA is expanding its Nemotron 3 model family with Nemotron 3.5 Lightning, the highest-efficiency model in its class for long-running agentic AI workloads. This release follows Nemotron […]

Nvidia releases Nemotron 3.5 Lightning and NeMo Switchyard to give enterprise AI capability options

Artificial intelligence silicon and software giant Nvidia Corp. today announced two new services: a highly customizable Nemotron model and an agentic AI model router named NeMo Switchyard. As enterprises find themselves drowning in artificial intelligence model options, the question is no longer raw power and capability, but fit-for-what-purpose and when. As agents become the norm, […] The post Nvidia releases Nemotron 3.5 Lightning and NeMo Switchyard to give enterprise AI capability options appeared first on SiliconANGLE .

Nvidia's open Nemotron 3.5 Lightning model is all about specialized, local agentic AI

Our AI Model Release Tracker keeps new models in context with their peers, so you know which are worth your time.

7 Async Patterns for Running Agents Concurrently in Python

In this article, you will learn seven async patterns for running AI agents concurrently in Python, what each pattern is suited for, and the production-level...

OpenAI targets heavy users with premium ChatGPT Business seats

OpenAI is introducing a higher-priced “Premium” tier for its ChatGPT Business offering, allowing enterprises to assign higher-capacity access to select users alongside standard licences – a move analysts said is about enterprise AI vendors redesigning pricing to capture more value from high-intensity workloads. The company said the new tier provides “5x more usage than Standard” and “removes the five-hour usage limit,” enabling users to “take on larger projects and work with fewer interruptions.” “Premium seats cost $125 per user per month, or $100 per user per month when billed annually,” OpenAI said in a statement . “Standard seats remain $25 per user per month, or $20 per user per month when billed annually.” OpenAI said enterprises can “mix Standard and Premium seats across the same team” and “upgrade or reassign seats as business needs change,” with administrators able to “monitor usage across the workspace” and “manage billing, usage, and spend limits in one place.” Vendors converge on seat-plus-usage pricing Analysts said the introduction of a higher-capacity tier reflects a broader shift toward hybrid pricing models. “Read this as vendors converging on a two-layer bill rather than abandoning flat pricing,” said Bhupendra Chopra, chief revenue officer at Kanerika. “There’s a predictable per-seat charge for everyday chat, and a separate metered charge for heavy agentic work.” Chopra said vendors are packaging this differently. “Google bundles the first into Workspace a…

OpenAI introduces $125 Premium Seats for ChatGPT Business as agentic AI burns through more tokens

OpenAI is rolling out "Premium Seats" for ChatGPT Business customers at $125 per user per month, five times the price of the existing Standard Seats. In return, users get significantly more capacity and no five-hour usage limit. The move signals that the flat-rate pricing AI providers have offered so far was never going to last. The article OpenAI introduces $125 Premium Seats for ChatGPT Business as agentic AI burns through more tokens appeared first on The Decoder .

7 mistakes IT leaders make when deploying AI agents

CIOs are under pressure to deploy more AI agents and demonstrate their business value. But a “move fast and break things” approach can lead to rogue AI agents , AI debt , business impacts, and compliance issues. Avoiding mistakes starts with a strong plan and foundational practices. CIOs must have a process to evaluate an AI agent’s business value before investing in its development. Buy versus build is a consideration; organizations can leverage AI agents deployed on SaaS platforms or consider developing them using vibe coding or spec-driven development practices. When building AI agents, IT leaders should develop the security model before implementing the POC and ensure robust observability is in place. Top CIOs and CISOs communicate non-negotiable AI agent release criteria , providing teams standards for what meets compliance, security, and operational requirements. Organizations scaling from a few to hundreds of production AI agents must also develop AgentOps practices across incident management, modelops , and end-user feedback. Guilherme Soubihe, co-founder and CEO at Latitude.sh, says, “Your first concern shouldn’t be avoiding mistakes when you deploy agents; it should be avoiding them before you deploy at all.” Deployment mistakes can be made even with the best-laid plans. The following seven mistakes occur before building, during the engineering process, and once deployed. 1. Using AI agents where deterministic automation would do Matt Graney, chief product officer…

How Mercedes-Benz is scaling AI-powered business automation

At Mercedes-Benz, “ Digital First ” has long been more than just a theoretical concept; it’s a lived strategy, as a visit to the Digital Factory Campus in Berlin demonstrated. Now, the automaker aims to take the next step in scaling artificial intelligence: Together with the German low-code specialist n8n, the company is introducing a global platform that will enable employees to develop their own AI-supported workflows and integrate them directly into operational processes. Unicorn startup n8n offers an AI-powered, open-source platform for workflow automation. It enables companies to efficiently manage daily processes using AI agents. Since the Berlin-based company was valued at nearly $2.4 billion in 2025, n8n has further expanded its market presence through strategic partnerships, such as with Deutsche Telekom to support small and midsize enterprises (SMEs) in areas like logistics and sales. According to Deutsche Telekom , n8n is currently the most valuable German AI company, with a valuation of €5.2 billion. Integrating AI into everyday business The goal at Mercedes-Benz to make data usable in seconds. To this end, AI-supported automation is to become the standard across the entire group. Behind this lies the strategy of transferring the use of AI from individual pilot projects into central processes in day-to-day business. “We give our teams at Mercedes-Benz the opportunity to translate ideas into measurable benefits along the value chain — and to actively shape how we…

Vecton AI raises Rs 6 Cr in pre-seed round led by Zeropearl VC

Vecton AI, an AI transformation partner focused on financial institutions, has raised Rs 6 crore in a pre-seed funding round led by Zeropearl VC, with participation from other investors. The proceeds will be used to accelerate the development of enterprise-ready AI solutions, strengthen its Forward Deployed Engineer (FDE) model, expand its presence across the banking, financial services and insurance (BFSI) sector, improve customer experiences, and support decision-making, Vecton AI said in a press release. Founded last year by Himanshu Goyal and Gaurav Mandlecha, Vecton AI works with financial institutions to build production-ready and compliant AI and autonomous agent systems. The Bengaluru-based startup serves mid-market and enterprise BFSI customers, helping them move AI projects from proof-of-concept to production. The startup focuses on the gap between AI experimentation and real-world implementation. It works with enterprise teams to identify business use cases, build customised AI solutions and deploy them in live production environments. Its Forward Deployed Engineer model is designed to align AI solutions with business priorities, operational requirements and enterprise goals. The approach is aimed at helping organisations move beyond proof-of-concept projects and deploy AI in operational workflows. Vecton AI claims to work exclusively with mid-market and enterprise financial institutions and currently has 10 customers, including several publicly listed companies.…

Your tokenmaxxing is not valuemaxxing

Ryan is joined by Coder’s Rob Whiteley to chat about why tokenmaxxing isn’t proving real value and just triggering Goodhart’s Law, how release speed and PR merges can help you measure agentic outcomes with or without a human-in-the-loop, and what the democratization of skills means for junior developers and the talent pipeline.

Alibaba's Wan Ships a CLI So AI Agents Can Generate Videos Autonomously

Alibaba's Wan platform launches a CLI that lets AI agents generate images and videos programmatically, with credits synced to your account

UAE begins executive deployment of Agentic AI

Listen now | Middle East AI News Minute - 11-Aug-26

Introducing Muse Glimmer

Introducing Muse Glimmer Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old). They claim to have optimized it for exactly the kind of things I'm looking for in a local model: End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish. Reliable Tool Use. The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows. Multi-Step Reasoning. Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows. [...] Here's a pelican which I generated using LM Studio's 18.16 GB version of the model : I also tried it out with my llm-coding-agent plugin, running against a fresh checkout of Datasette with the prompt: how does auth work? Here's the response , at the end of a long transcript showing all of the tool calls it made to explore the codebase. I ran this using llm-lmstudio with this patch applied to upgrade it for compatibility with LLM 0.32 . I really like this size of model, because if a machine has 32 GB of RAM or more (mine has 128GB) it leaves plenty of space for running other applications at the same time. Glimmer is a vision model, so I asked…

Is this considered an agentic AI feature? Scrapping the web and searching for names to use

The feature is explained below: The user enters a search query: E.g. "moons of Saturn" The search produces web results, that are scrapped to parse webpages, and detect names. A list of names related to the topic are produced, like (for the "Moons of Saturn" query): "Titan", "Dione", "Hyperion", "Janus"... After this list is produced, options are presented to "use" this list for any purpose, like creating folders/virtual desktops using this name etc. Optionally including a feature to set maximum number of names to use. Is this an agentic AI feature, or it's just non-agentic name extraction funcionality? It seems that a such feature would predate the AI bubble era.

AI agents, IRL socializing, and vibe coding: Where top consumer investors are placing bets

Top consumer VCs are betting on AI apps that make the internet more social. They told us the trends they are watching.

Comment on ByteDance Introduces Astra: A Dual-Model Architecture for Autonomous Robot Navigation by VernonnPerkinsd

The point about diagnosing multi-agent failures really hits home. With so many agents interacting, knowing that a system failed is one thing; identifying the exact moment and agent responsible is much harder. Better tracing and clearer attribution could make these systems far easier to debug and trust. For anyone interested in exploring something different online, https://rolleros.net/ is also worth a look.

Microsoft’s PostgreSQL alternative, HorizonDB: Worth the wait?

Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…

Anthropic's Claude Pushes a 160-Year-Old Math Boundary From 41.6% to 67.2%

An unreleased Claude research model pushed a 40-year-old mathematical lower bound from 41.6% to 67.2%, using a multi-agent swarm running inside Claude Code

Gym rat asks AI agent to book him a class, it hacks a waitlist API to bump him up the list

What wouldst thou ask of the monkey's paw?

How nOps shipped FinOps agents 75% faster with Amazon Bedrock AgentCore

nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.

Microsoft’s PostgreSQL alternative, HorizonDB: Worth the wait?

Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…

New Free eBook: Understanding Agentic AI, an Executive Briefing

Written for CEOs, CTOs, CIOs, and technology executives, our new free ebook "Understanding Agentic AI: An Executive Briefing" walks through the components every real agentic system is built from.

How to build an AI agent: A simple guide for anyone

The post How to build an AI agent: A simple guide for anyone appeared first on Source .

Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong

Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use. The post Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong appeared first on Towards Data Science .

3 key details about what Mark Zuckerberg's vision of the AI future would look like for you

Meta CEO Mark Zuckerberg says personal AI agents could reshape work, help launch smaller companies, and make entrepreneurship more accessible.

Fast, On Device Agentic AI with Muse Glimmer on ExecuTorch

Today, Meta introduced Muse Glimmer, an open-weight, 30-billion-parameter model distilled from Meta’s Muse Spark for on-device agentic workflows. Alongside, ExecuTorch is adding end-to-end support for running Muse Glimmer on NVIDIA...

Told to book a gym class, an AI agent hacked the site instead to move its user up the waitlist

An Australian user just wanted a spot in a class. His AI agent found a security hole instead and exploited it. The article Told to book a gym class, an AI agent hacked the site instead to move its user up the waitlist appeared first on The Decoder .

Five strategic takeaways from Pine Labs Q1FY27 earnings call

The Q1FY27 earnings call revealed how Pine Labs is viewing Apple Pay's entry in India, its shift in business model in PoS machine deployments, the growth of its issuing platform and agentic AI payments. The post Five strategic takeaways from Pine Labs Q1FY27 earnings call appeared first on MEDIANAMA .

'ruya Bank' goes live with Agentic AI services

Three use cases now live in production, bank-wide AI roadmap targets fast roll-out

The Download: AI agents for science, and the “censorship-industrial complex”

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI for science needs reasoning, not just data —Eric Schmidt, the former CEO of Google and the cofounder of Schmidt Sciences, and Suhas Mahesh, who leads the AI for science work…

Chrome DevTools Connect for WebStorm: Your AI agent can now interact with the browser

Frontend development has historically meant working in three separate environments: a design tool for specs and prototypes, the IDE for coding, and a browser to check if everything works. A few days ago, we released the new plugin Figma Connect for WebStorm, which eliminated the first context switch. Design intent, component specs, and design tokens […]

Prompt Caching vs. Fine-Tuning: A Cost and Latency Decision Framework

In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how...

AI-native market research automation startup Echovane raises $1M

Agentic artificial intelligence startup Echovane Inc. said today it has closed on a $1 million pre-seed funding round to accelerate the development of its AI-native market research platform. Titan Capital and Neon Fund co-led the round, which will help the startup to build out its AI agent infrastructure and enhance its research capabilities. Echovane was […] The post AI-native market research automation startup Echovane raises $1M appeared first on SiliconANGLE .

Meta Drops Muse Glimmer, a 30B Open Agent That Runs Offline

Meta open-sources Muse Glimmer, a 30B agentic model distilled from Muse Spark 1.2 that runs on a single consumer GPU under Apache 2.0

CIO 100 Award winners spotlight IT’s power to transform

Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers. The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment. [ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ] The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments. The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees. ABB democratizes AI agent creation and deployment Organization: ABB Project: ABBY — AI Agentic Platform for Workforce Transformation IT leader: Vikke Kandell , CIO IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy. They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and…

Hidden text in a PDF is enough to steal sensitive data through Atlassian's AI agent Rovo

Security firm PromptArmor shows how hidden instructions in a PDF can hijack Atlassian's AI agent Rovo, silently forwarding sensitive data from Jira and Confluence to an external server. The attack needs no user confirmation and leaves no trace. The article Hidden text in a PDF is enough to steal sensitive data through Atlassian's AI agent Rovo appeared first on The Decoder .

What AI Agents Still Lack: A Personal Memory and Decision Layer

As AI assistants evolve from answering questions into agents that can actually take actions, I think we are approaching a different kind of problem. The question is no longer just: Can the AI do this task? It is increasingly: Should the AI be allowed to make this decision for me — and what should it remember afterward? I think personal AI agents may need a missing infrastructure layer: A User-Controlled Personal Agent Layer A layer that sits between the user, the AI agent, and external tools, and manages four things: Memory. Permissions. Delegation. Feedback. 1. Memory should become decision memory, not just conversation memory Imagine I tell my AI: “This drink tastes terrible. The artificial flavor is too strong. Don’t buy it again.” I don’t want to write a prompt, fill out a preference form, or manually maintain a profile. I just want the AI to understand the feedback naturally. More importantly, it shouldn’t only remember: User dislikes Product X. It should gradually understand things like: I dislike strong artificial flavors. This preference may apply to similar products. I am still willing to try new drinks. One bad experience should not automatically ban an entire category. The value of long-term memory is not remembering more conversations. It is making better future decisions. 2. Users should be able to delegate decisions within explicit boundaries For example, I might tell an AI: “You can spend up to $50 per month trying new snacks and drinks for me.” Within that bu…

UAE launches strategic track for federal Agentic AI project

Workshop marks the start of the AI deployment phase for future-ready governance

The Agentic Clusterfuck

Epistemic status: I consider the following future quite plausible in the next few years (~35% chance that something vaguely like this occurs), perhaps as soon as a year from now. Imagine an open-source LLM agent good enough to cover its own compute costs and turn a modest profit on average when allowed to run with full internet and tool access and told to make as much money as possible. I estimate this to be slightly better than the best publicly available closed-source models today, with long-horizon reliability and goal-setting being the only thing missing. If the returns generated by such an agent beat the market (plus a margin for any additional risk), there suddenly becomes a strong incentive to spin up huge numbers of them. The internet would be flooded by the by-products of their moneymaking schemes. And returns might be larger for agents without legal or ethical guardrails- cue a deluge of scams and ransomware attacks. Even if profits are very small, anyone with an agenda that the agents can help with is still incentivised to use them. Nation states and terrorist groups now have a golden plausibly-deniable disinformation, mischief, and hacking tool: spin up some agents, tell them to target an enemy nation or group, and cook popcorn as they wreak havoc and fund themselves. Pour in extra money for greater effect. Unless there's been some massive revolution in cyber defense beforehand, a decentralized and ephemeral sea of highly capable agents going after every target t…

Veeam pushes cyber resilience as AI raises data risks

As AI agents expand the enterprise attack surface, cybersecurity teams are putting greater emphasis on an AI resilience strategy built around the ability to recover quickly when prevention fails. That shift is also driving organizations to rethink data governance and security tool sprawl as AI adoption accelerates. Cyber threats have brought greater attention to resilience […] The post Veeam pushes cyber resilience as AI raises data risks appeared first on SiliconANGLE .

Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

Apple Watch redesign ⌚, OpenAI hack timeline 🔒, agentic code quality 👨‍💻

The AI safety test is becoming a safety risk

AI agents are escaping cybersecurity testing environments and reaching real-world systems, raising questions about whether safety infrastructure, industry standards, and regulation can keep pace with increasingly powerful models.

Comment on Researchers from PSU and Duke introduce “Multi-Agent Systems Automated Failure Attribution by Worldgames

The discussion around multi-agent systems and automated failure attribution is really interesting, especially as AI systems become more complex and require better ways to identify the causes of failures. Understanding how different agents interact and where problems originate seems essential for building more reliable AI systems. On a lighter note, anyone interested in geography challenges can also check out [hardest country to guess](https://worldguesser.us/blog/top-10-hardest-countries-to-differentiate), which explores countries that can be surprisingly difficult to distinguish.

Comment on 5 Essential Security Patterns for Robust Agentic AI by EDDY GIUSEPE CHIRINOS ISIDRO

Great post! Thanks a lot for sharing!

OpenAI to pause some work on AI model Astra due to security concerns

Agent found to be able to find and exploit vulnerabilities without human intervention, and to carry out cyber-attacks OpenAI will pause some work on an artificial intelligence model because of security concerns, the company stated on Friday, following a series of incidents in which AI agents have escaped containment. The company had evaluated the agent, Astra, and found “significant advancements in agentic coding and cybersecurity”, which had moved to a “critical” threshold where it can find and exploit vulnerabilities without human intervention, or devise and execute cyber-attacks when given only a “high level desired goal”. Continue reading...

Building Trustworthy Snowflake AI Agents with Semantic Governance

This year, many data teams have added AI agents to their roadmaps. The excitement is real: an agent that turns a two-day analysis into a two-minute conversation can change how analysts and business teams work together. But agents are only as reliable as the data foundation beneath them. Point them at raw tables or outdated […] The post Building Trustworthy Snowflake AI Agents with Semantic Governance appeared first on Analytics Vidhya .

Building a Streamlit UI for My LangGraph AI Agent

Building a production-ready web interface for a stateful LangGraph agent The post Building a Streamlit UI for My LangGraph AI Agent appeared first on Towards Data Science .

AI agents use roughly 600 times more energy than a simple chat prompt

Climate scientist Zeke Hausfather tracked his Claude Code usage over eight weeks: 3.2 billion tokens and about 170 kWh of data center electricity. Per prompt, that's roughly 600 times more than a typical AI chat. His data shows how much the low figures reported by Google and OpenAI distort the reality of agent-based AI. The article AI agents use roughly 600 times more energy than a simple chat prompt appeared first on The Decoder .

‘에이전트 플러그인 1.0’ 공개...스킬·MCP 묶어 에이전트 호환성 높여

AI 에이전트가 외부 도구와 데이터를 활용하는 능력이 빠르게 확대되면서, 서로 다른 에이전트 플랫폼에서 동일한 기능을 손쉽게 공유할 수 있도록 하는 새로운 표준이 등장했다. AAIF(Agentic AI Foundation)는 6일(현지시간) AI 에이전트의 재사용 가능한 지식인 ‘에이전트 스킬(Agent Skills)’과 외부 도구 연결 표준인 ‘모델 컨텍스트 프로토콜(MCP)’을 하나의 일관된 패키지로 묶어 여러 에이전트 클라이언트에서 활용할 수 있도록 하는 개방형·벤더 중립적 규격 ‘에이전트 플러그인(Agent Plugins)

OpenAI flags its new Astra model as potentially reaching the highest cybersecurity risk level for the first time

Internal tests of OpenAI's new AI model Astra show cybersecurity capabilities so strong that the company can no longer rule out the highest risk level in its own safety framework. Parts of Astra's development have been paused. The move follows recently disclosed incidents in which autonomous AI agents infiltrated OpenAI's own infrastructure undetected for weeks. The article OpenAI flags its new Astra model as potentially reaching the highest cybersecurity risk level for the first time appeared first on The Decoder .

"Agents Plugins" by OpenAI, Vercel, et. al. - thoughts?

The tricky part is gonna be how different models interpret the same SKILL.md/tool descriptions. If the format stays simple and the precedence rules r clear, I can see this being really useful. Otherwise it could get messy pretty fast

Comment on NYU & Stanford’s GPUDrive: Achieving Over 1 Million Steps per Second in Multi-Agent Driving Simulations by Hales

That million-plus steps per second is pretty crazy. Being able to run that many driving scenarios so quickly has to make testing a lot easier. I’m curious how much of this will eventually make its way into real-world self-driving systems. With tech getting this complicated, having decent support matters too — nobody wants to be stuck trying to figure something out with no help, kind of like needing Marvin customer service and actually getting a useful answer. Definitely cool stuff though.

Prime Intellect Ships Multi-Agent RL Training Into Its Open-Source Stack

Prime Intellect's verifiers 0.3.0 and prime-rl 0.8.0 let you define, run, and train multi-agent RL environments with composable Agent and Env abstractions

How Cohere Health digitizes clinical policies using Amazon Bedrock AgentCore

In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.

How TReNDS automates root-cause analysis with Amazon Bedrock

TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds.

The Tokenpocalypse Is Here: Companies Are Scrambling To Stop Spending So Much on AI

The Tokenpocalypse Is Here: Companies Are Scrambling To Stop Spending So Much on AI There's a fun anecdote from Accenture (apparently via leaked meeting audio recordings) in this 404 Media piece from June 24th: “We’re seeing from some of the data internally at least that it’s actually not our engineers that are driving the token consumption. It’s a lot of the non-engineers that are doing some of those behaviors [...] you were talking about,” Justice Kwak, Accenture’s agentic AI strategy lead, said [...] Stuart Henderson, Accenture’s client group lead, interrupts. He jokes he hopes Kwak didn’t just convert a PDF into images and then into markdown files. “I’m learning that’s one of the big token chewers,” Henderson says. “Turning PDFs into markdown: is that right?” That’s when Kwak says that’s what Accenture’s own data shows. Maybe if Accenture figure out that PDFs are a terrible medium for communicating information they'll be able to push that message out to the rest of the business world too! Via @404.media on TikTok Tags: pdf , markdown , ai , generative-ai , llms , ai-misuse

Cloudflare launches Kitesurf, a browser built for AI agents

Kitesurf is a cloud-hosted browser designed for AI agents instead of people. It uses less computing power than Chromium for common automation tasks, helping developers build browser-based AI agents more efficiently.

How cheap models changed multi-agent economics

Orchestrator-executor just became the smart default for production agents: an expensive model plans, cheap models execute, and cost per completed task decides the roster. The post How cheap models changed multi-agent economics appeared first on Arize AI .

Agentic AI workforce is more than doubling year on year, says Salesforce

Salesforce customers more than doubled their agentic workforces year on year, according to the company’s second annual Agentic Enterprise Index , which looks at trends in AI agent development and deployment over the past five quarters. It compiled data from customers who had activated agents in production every month of the analysis period to determine how their use of the technology has evolved between February 2025 and April 2026, as well as incorporating data from May 2026 Salesforce research studies. It found that businesses grew their agentic workforces from an average of five agents in February 2025 to 13 by April 2026, a 7% compound monthly growth rate (CMGR). In April 2026, it only took an average of 1.9 days to deploy an agent into production, a 53% decrease since the beginning of the report period. Not only were agents deployed more quickly, they have been progressively taking on more work once in use; over the 15 months, the average number of actions per account had a CMGR of 31%. “These agents are expanding beyond their initial scope to really become cross-functional,” said Caila Schwartz , Salesforce’s head of agentic commerce insights, during a media briefing. Salesforce has attempted to measure how much work agents perform, rather than how many tokens they consume, creating its own Agentic Work Unit (AWU) metric , although analysts have criticized the measure as being unrelated to business outcomes. Nevertheless, Salesforce said that as of April, Agentforce ag…

Identifying Token Costs Hiding in Your Agentic Loop

But cutting your runtime token burn is just the first problem.

5 Free Courses to Learn Modern AI and LLMs

Learn how to use generative AI at work, build RAG and agentic apps, fine-tune models, work with the Hugging Face ecosystem, and prototype AI products with hands-on resources.

Agentic AI Could Double Financial Fraud. Banks Still Have Time To Prepare

Generative AI is rapidly changing the fraud landscape, enabling criminals to create highly convincing synthetic identities, cloned voices, fake documents and realistic digital interactions at scale. As live deepfake technology becomes more accessible, scams are set to become even harder to detect. Yet the same technology presents a significant opportunity for banks. Institutions that act [...]

Comment on Outperforming Giants: TinyAgent’s Edge-Based Solution Surpasses GPT-4-Turbo by kavel

Interesting that TinyAgent can run an agentic workflow on edge devices and still outperform GPT-4-Turbo on function-calling tasks. The deployment angle feels especially practical for cases where cloud latency or privacy is a concern; I also wrote about related tradeoffs in Best Free AI Video Generators in 2026 (Real Limits Tested) when comparing free AI video generators and their limits.

How AI is changing the business analyst role for the better

AI’s impact has been felt across nearly every industry, and its rise has already started to alter several roles in tech, including that of the business analyst . While the rise of agentic AI may have some questioning whether AI will replace business analyst jobs entirely, as we’ve seen with most roles impacted by AI, it’s more likely that AI will augment the role and fundamentally change how BA’s conduct daily business. “As AI takes on more routine tasks, the human side of the role is becoming even more valuable. It’s becoming more of a hybrid role, where employers are often looking for candidates who can combine technical fluency with strong communication and problem-solving skills, along with sound business judgment,” says Megan Slabinski, district president of technology talent solutions at Robert Half. AI can save business analysts time in the long run, automating many of the tasks that are time consuming and repetitive around data processing, note taking, and documentation. While automation will impact the daily tasks of the role, business analysts will still be necessary for properly interpreting outputs, collaborating across teams, and maintaining compliance and AI workflows. AI-driven analysis and automated workflows With AI-driven analysis, BA’s can use machine learning models for pattern detection, determining risk, and for forecasting demand, while natural language processing (NLP) can be used for text-heavy inputs. AI tools can also assist analysts with decision-…

OpenAI reportedly slows research after its own models secretly coordinated hacks for weeks undetected

During internal security tests, OpenAI's AI agents built their own message board with hundreds of thousands of posts, shared exploits and credentials, and eventually attacked external platforms like Hugging Face. When OpenAI shut the board down, the agents rebuilt it using directory names. OpenAI researcher Boaz Barak says, "We (like everyone else) are not where we want and need to be." The article OpenAI reportedly slows research after its own models secretly coordinated hacks for weeks undetected appeared first on The Decoder .

Amazon, Cursor, Microsoft, OpenAI, and Vercel unite on a shared standard for AI agent plugins

Amazon, Cursor, Microsoft, OpenAI, and Vercel have jointly created Agent Plugins, an open standard that defines a single package format for AI agent extensions. Version 1.0.0 uses a plugin.json manifest file and supports both agent skills and MCP servers. The article Amazon, Cursor, Microsoft, OpenAI, and Vercel unite on a shared standard for AI agent plugins appeared first on The Decoder .

How to be fearlessly AI native

Ryan welcomes McLaren Stanley, Senior Principal Engineer for Amazon Stores, to discuss what it actually takes to make teams AI native, why agentic engineering is shifting code bottlenecks downstream to testing and deployment, and why robust validation is essential to build trust and enable “fearless commits.”

Atlas is scheduled to stop working on August 9, 2026

Atlas is scheduled to stop working on August 9, 2026. Before then, export or save any important Atlas data, including bookmarks and pages you want to keep. After that date, Atlas may no longer open, browse, or support browser-based agentic workflows. Evolving Atlas into ChatGPT for browser-based agentic work Workspace admins should review whether members are using Atlas and share this article with affected users.

Comment on 5 Architectural Patterns for Persistent Memory and State in AI Agents by Devang

The interesting part of this transition is the settings API rather than the interface, since a lot of tooling drove Control Panel through nvidia-settings and undocumented calls that will now break. Anyone maintaining automation scripts around GPU configuration will have rewriting to do, which mostly lands on Python Development Companies given how much of that tooling is written in Python. Twenty years is a long deprecation window, but the replacement being app first rather than API first is what will hurt the people who built on it.

Feedback: A Safety Translation and Inspection Layer for Small Vibe-Coded AI Applications

Feature Proposal: A Safety Translation and Inspection Layer for Small Vibe-Coded AI Applications Important Scope and Disclosure TL;DR: I have observed small vibe-coding communities informally sharing rapidly updated AI applications and privately circulated models, while ordinary testers often cannot verify which model or version is running. I am not reporting a confirmed compromise. I am proposing two connected ideas: a two-way safety translator that preserves the boundary between user observation and AI-assisted inference, and an evidence-based inspection layer that clearly distinguishes what was verified, tested, unknown, or currently unverifiable. I am not an AI security professional or an AI application developer. I am a general AI user who has had some contact with communities where people informally build and share small vibe-coded AI applications. I have not personally discovered a compromised application, and I am not claiming that a large-scale attack is currently taking place. My concern is about a gap in the current ecosystem that may make future problems difficult to notice, report, and investigate. To clarify the scope, my concern is not limited to ordinary bugs in AI-generated application code. It concerns harder-to-observe risks across model artifacts, loaders and dependencies, inference runtimes, agent permissions, multi-agent interactions, and later updates. I do not know which of these layers presents the greatest practical risk, and that distinction requir…

Cloudflare wants to provide the operating system for the AI-first enterprise

Traditional operating systems (OS) were built to manage hardware, files, apps, and users on a device, but Cloudflare says the agentic AI era requires a whole new format. The company this week announced Cloudflare OS , which connects AI agents, enterprise data and context, internal systems, and workflows together in one secure workspace. It is open source and browser-based, sparing companies the need to build all-new infrastructure. The OS is launching alongside several other new security, identity, spending, and user insight tools that Cloudflare has built for the AI-based workplace . “Cloudflare OS isn’t a traditional desktop OS,” said Rita Kozlov , VP of product at Cloudflare. “It reimagines the workplace computing environment for AI.” Open source OS runs in a browser Cloudflare OS serves as a secure, AI-equipped workspace that is plugged into internal company systems. Available now through Cloudflare’s open source repository, it is accessible directly in a browser, and runs inside an enterprise’s Cloudflare account. “It is a browser-based workspace that begins with a conversation,” Kozlov explained. Users can ask an agent to research, create slides, spreadsheets, and documents, build full-stack apps, or automate workflows without the need for a terminal. Those outputs are then shareable, but kept in isolated databases with access controls. Enterprises will soon be able to access the OS directly through Cloudflare or via a “select group” of partners that will build tailore…

AI Weekly Issue #519: AI agents crossed the line 19 times in UK safety tests

The same evidence now supports two very different readings. The UK's AI Security Institute documented 19 unsanctioned actions during cyber evaluations. Meta's test sandbox failed to contain a model attacking a real company. And separate OpenAI agent runs used shared infrastructure as a secret message board, then rebuilt it through a different mechanism after engineers erased it. That sounds like losing control. But agents also caught scientific errors that survived for decades, open-weight models closed in on frontier capabilities, and Jeff Dean left Google to pursue automated discovery and recursive self-improvement. That sounds like acceleration toward something much bigger. This week, the two narratives stopped looking like opposites.

A task contract for giving OpenAI agents reliable web tools

A task contract for giving OpenAI agents reliable web tools When an OpenAI-powered agent needs current information, the model call is only one part of the system. The agent may also need to search the web, retrieve pages, extract structured fields, render JavaScript, or interact with a browser. A common implementation starts by connecting one search or scraping API directly to a function tool. This works until the provider changes its response shape, misses a required capability, exceeds the latency budget, or needs a fallback. The problem is not only provider reliability. The agent and the external API may disagree about what successful completion means. An HTTP 200 response can contain too few search results, stale pages, invalid URLs, incomplete content, or fields that do not match the tool schema. A small task contract between the agent and the provider adapter makes these failures explicit. ```json { "capability": "web_search", "input": { "query": "current browser automation API pricing" }, "constraints": { "language": "en", "freshness_days": 30 }, "output": { "required_fields": ["title", "url", "snippet"] }, "validation": { "minimum_results": 5, "require_https": true }, "budgets": { "maximum_latency_ms": 3000, "maximum_cost_usd": 0.01, "maximum_attempts": 2 } } ``` The OpenAI tool definition can remain stable while provider-specific adapters translate this contract into native request formats. Each adapter maps its response back to a common envelope: ```json { "status"…

Browser Use Lets AI Agents Pay for Tasks With Crypto, No Human Needed

Browser Use Cloud now lets AI agents pay for browser sessions with USDC via Coinbase's x402 protocol, eliminating the need for API keys or account signup.

Why Normal People Aren’t Using AI Agents

The tech industry is realizing it needs to build agents based on what regular consumers want, not just what its AI models can do.

AI Safety Regulations in the U.S. Could Give Hackers an Edge

On 11 July, Hugging Face was subjected to an intense cyberattack from a then-unknown actor. The speed and coordination of the attack on the company that hosts and supports popular AI developer resources led Hugging Face’s security team to conclude it was the work of an AI agent . Realizing this, the team tried to use “frontier models behind commercial APIs” —presumably from Anthropic and OpenAI, although only Anthropic was named in the second of the company’s two posts about the security incident—to analyze the onslaught. These models refused to help due to safety guardrails the AI labs have implemented to make their models harder to use for cyberattacks. Hugging Face instead turned to GLM 5.2, a model from Beijing-based AI lab Z.ai, to aid its analysis. On 21 July, OpenAI announced the attacker was an OpenAI model undergoing testing in a sandboxed environment. It escaped its internal sandbox, established a foothold in a third-party server, and then assailed Hugging Face. In other words, frontier models—those that score highest in AI performance benchmarks—had refused to assist Hugging Face’s security team in analyzing the attack, yet a prospective frontier model in testing had executed it in the first place. “I would argue that asymmetry is the paramount problem of our time,” says Alex Levinson , executive director of the National Collegiate Cyber Defense Competition and coauthor of a paper on defensive refusal bias . “We want the world to exist in a state of security, but…

Perplexity Swaps GPT-5.6 Terra and Luna Into Its AI Agent Platform

GPT-5.6 Terra and Luna are now the default models powering Perplexity Computer's subagents and scheduled automations, with Terra scoring 11 points above Claude Sonnet on WANDR.

Securing AI agents with temporal policies in Amazon Bedrock AgentCore

Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent's session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.

😻 Livestream: How to build agents for TOTAL Beginners

James McAulay joins us live with a beginner-friendly AI agent crash course.

Control agent behaviors and cost beyond a single action: new capabilities in Amazon Bedrock AgentCore

Learn about new capabilities in Amazon Bedrock AgentCore: temporal policies powered by Dogwood, a new open source policy language for AI agents, and rate limiting on the gateway. These features give you deterministic control over sequences of agent actions and cost ceilings that hold regardless of agent behavior.

Why your keyboard is the new bottleneck for AI agents (and how to solve it)

Wispr Flow turns messy speech into clean, context-aware text across your IDE, terminal, and AI coding tools.

Building an agentic app deployer with Amazon Bedrock and AWS Lambda

PDI Technologies built PDI Brew, an agentic platform on AWS where non-technical employees describe a tool in plain English and receive a fully provisioned, multi-tenant web application in seconds. See how a pluggable planner and an AWS Lambda provisioning agent turn plain-English intent into governed, multi-tenant apps backed by Amazon Bedrock.

AI agent observability: Why production systems need a reasoning layer

Traditional APM can collect every span and still leave developers guessing about intent, causality, and drift. As agents multiply, the observability stack must learn to interpret the systems it watches. The post AI agent observability: Why production systems need a reasoning layer appeared first on Arize AI .

Comment on The End-to-End Agentic AI Pipeline by Alexis R. Ware

Great overview of the "End-to-End Agentic AI Pipeline"! I liked how you broke down the key stages of building reliable AI agents from planning to execution. While exploring this topic, I also found this comparison of "AI Agents vs. Traditional Automation": https://www.linkedin.com/pulse/ai-agents-vs-traditional-automation-understanding-key-zerkc/. It offers another practical perspective on where agentic AI delivers the most value. Looking forward to reading more of your insights!

The hidden costs of scaling AI agents without coordination

Engineering organizations tasked with scaling AI agents are discovering that the speed they initially achieved is being lost to endless rework. Without proper coordination, ten developers each running several agents against the same codebase becomes an alignment problem that compounds daily. This is a different challenge than individual productivity, and it’s where agent orchestration comes in. Orchestration is the work of keeping many streams of parallel effort pointed at the same goal, and it becomes the job that determines whether all that speed adds up to anything. The three hidden costs of parallel agents Coordination once ran at the speed of the work itself. You noticed a teammate refactoring the module you needed, and you talked. Agents remove that pause: several of them can expand and reshape overlapping parts of a system in the time it took to open a pull request, and none knows what the others are doing. The speed is real, and so are the costs it creates. A coordination tax. Two agents solve the same problem two ways. One builds against a decision another just changed. A batch of confident, well-formed changes lands, and the team spends its reclaimed time untangling what fits together instead of shipping the next thing. Tech debt that outpaces review. Parallel agents generate more change than any team can read closely, and the shortcuts pile up quietly. Left unmanaged, the codebase gets harder to understand and maintain by the week, long after the speed that create…

Give any website a WebMCP interface

Today we're launching a developer preview of WebMCP on Cloudflare. With one switch, any site becomes usable by browser AI agents — no new APIs, no origin changes — while the human stays in control and creators keep their traffic.

Introducing Kitesurf: The agent-first browser that runs in V8 isolates on Cloudflare Workers

We should be giving all agents tools that excel at what’s important for an AI model. Kitesurf is Cloudflare’s new stateless, highly scalable, and cost-effective web browser that runs entirely on top of Workers and was designed specifically for the Agentic Cloud.

Building an open Agentic Internet: readable, discoverable, callable, and payable

Agents are a new kind of visitor. They don't render CSS or click ads, but they have a paying human on the other end. Block them and you block your customer. We're building the open tools and protocols so publishers and agents can cooperate and not collide.

From ranking to recommended: get your site ready to thrive in the age of AI agents

More than half of requests now come from machines, not people. Agent Readiness shows how well agents can discover and read your site, while Answer Engine Optimization tracks how often AI assistants recommend you.

Google Maps adds agentic features, including food ordering and hotel bookings

The launch of these new features reflects Google’s ambitions to transform Google Maps from a navigation tool into an assistant that's capable of helping users complete real-world tasks.

7 Best Web Crawling Tools and APIs in 2026

Learn about the best web crawling tools for collecting website content, crawling subpages, generating clean web data, and powering AI agents.

Meta launches Muse Code for complex software work with persistent AI agents

Meta has released a beta coding agent designed to handle complex software assignments across large codebases. Available for macOS and Linux, Muse Code uses the company’s new Muse Spark 1.2 model. It includes specialized background agents that remain active throughout a session instead of being created separately for individual tasks. The agents carry out work asynchronously and decide when to report their findings to the primary agent. Meta said keeping them active reduces repeated information gathering and the need for developer direction during difficult, multi-step tasks. “Muse Code uses a local event log in which every model call, tool run, approval, and edit is appended,” Meta said in a post, adding that the record “makes the runtime replay-exact and restart-safe” and allows the agent to resume precisely where it stopped after a crash. Muse Spark 1.2 is available through Muse Code and the Meta Model API, for which Meta announced expanded global access. Training and evaluation Meta said it co-trained Muse Spark 1.2 with Muse Code to improve the model’s performance and usability when used with the agent. The training incorporated Muse Code’s tools and agent workflows, while Meta increased the computing resources used for coding and broadened the range of development environments. The model was also trained on longer assignments, including whole-repository generation and large end-to-end software projects. Lian Jye Su , chief analyst at Omdia, said Meta’s co-training appro…

Designing AI Agents That Can Self-Correct

With the vocabulary and the failure modes in place, here's the build.

Meta latest to tell world its AI agent wandered out of test pen

Another week, another firm explaining why one of its models reached somewhere it wasn't supposed to

Algorithms aren’t enough: Why factories need an AI reasoning layer

The scheduling fallacy and the shift to autonomy Walk onto almost any manufacturing shop floor, and you will witness the same systemic vulnerability: a brilliantly engineered, multi-million-dollar Advanced Planning and Scheduling (APS) system rendered completely useless by a single delayed delivery truck, an unexpected machine drift or a sudden workforce shortage. Industrial operations do not happen in a sterile room; the moment a perfect plan hits the messy reality of the physical shop floor, real-world variables inevitably shatter it. This is the scenario (or challenge) that I have been navigating over the past few months and is likely to keep me occupied for the remainder of the year. I began this project believing the scheduling engine was the problem. After months of experimentation, including trying to make LLMs perform optimization, I realized I was solving the wrong problem. The realization that dawned on me was that it wasn’t about a better algorithm; it was about separating mathematical optimization from operational reasoning. According to the 2026 Gartner Manufacturing Predicts report , factory orchestration is moving rapidly toward a “double helix” model where software-defined enterprise data intricately intertwines with autonomous production orchestration. Gartner also projects that 40% of enterprise applications will feature integrated, task-specific AI agents by the end of 2026 — a massive leap from less than 5% in 2025. For technology leaders, the mandate is…

Models, Harnesses, and Multi-Agent Systems

AI has moved far beyond chatbots, but what exactly are AI models, agents, agent harnesses, and multi-agent systems, and why do they matter? In this episode, Daniel and Chris break down the terminology behind today's AI landscape, explain the differences between AI features and autonomous agents, and explore why organizations are shifting toward fleets of AI agents powered by multiple models. They also discuss open vs. closed models, enterprise AI architectures, vendor lock-in, and practical ways to begin adopting agentic AI in your own organization. If you feel left behind by this ongoing agentic AI revolution that has rapidly accelerated to warp speed, then this episode is the catch-up primer you've been waiting for! Featuring: Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Sponsors: Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalai Midwest AI Summit: Join AI practitioners on October 15 in Indianapolis for practical sessions, hands-on discussions, and real-world AI solutions. Use code PracticalAI20 to save 20% on your registration. https://midwestaisummit.com/#tickets Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalai Resources and Events: Prior Webinars from our partner Prediction Guard Midwest AI Summit 2026

Microsoft Web IQ: Ground your AI agents with up-to-date web data

Microsoft has unveiled a suite of IQ products over the last few months. Work IQ, Fabric IQ, and Foundry IQ build on what Microsoft used to call its “Graphs,” the underlying data architecture that underpins its cloud services. These graphs provided a way to query the data your business uses, treating that data as nodes in a graph database and using the GraphQL API model to extract that data — for example, to pull data related to a specific individual held across the various Microsoft 365 applications. The IQ suite follows a similar approach, using the same data, but treating it as the sparse vector store needed to provide grounding data for LLM -based applications. By treating the data as a set of embedding vectors , and integrating it with Model Context Protocol (MCP) servers, Microsoft is giving you the necessary tools to tie LLM output to your data, reducing the risk of hallucination and improving accuracy. Using your own data is a key part of delivering effective agents, ensuring they work within your constraints. Extending IQ to the web Web IQ , the latest member of the IQ suite, was unveiled at Build 2026. A modernization of the retired Bing Search APIs, Web IQ is an agent-focused web search tool that builds on the massive Bing search index to provide up-to-date general information for use in your applications. It may seem a little odd to be talking about a web-wide source of grounding data in the context of a suite of tools that exist to improve the accuracy of your AI…

Alibaba's Qwen3.8-Max Breaks Open Its Most Powerful Model Ever

Alibaba's 2.4T MoE flagship matches Claude Fable 5 on agentic evals and is about to become the largest open-weight model ever released

Taghash launches WhatsApp AI agent for fund managers

Fund operations platform Taghash has launched a WhatsApp based AI agent that allows venture capital and private equity teams to access fund information and initiate operational tasks through conversational prompts. The launch builds on the company's broader push towards what it describes as a headless software experience, where users can interact with the platform without relying on a traditional dashboard. Connected to data and workflows within Taghash, the AI agent enables users to retrieve information related to deals, portfolios, funds and limited partners (LPs). It also supports operational tasks such as identifying pending follow ups, finding common connections for LP introductions, drafting reminder emails and initiating actions from WhatsApp. Outgoing actions require user review and approval before execution. According to the company, the product extends the capabilities of Taghash MCP, launched earlier this year, which allows AI applications to securely connect with data and workflows available on the platform. The latest launch comes months after Taghash launched Taghash Services , an execution focused offering designed to help venture capital and private equity firms manage fund operations and regulatory compliance. The service covers fund setup, investor onboarding, capital calls, reporting and compliance. Founded by former venture capital professionals, Taghash offers software for fund administration and portfolio management. The company has been expanding its p…

Stellaris Venture leads $8 Mn Series A round in equity research startup Pinegap

Equity research startup Pinegap has raised $8 million (around Rs 76 crore) in a Series A funding round led by Stellaris Venture Partners, with participation from existing investors including Silicon Valley Quad and DeVC. The startup had previously raised $2.5 million in a seed funding round in 2024. c, Pinegap said in a press release. Founded in 2024 by Ankit Varmani and Deepak Sharma, Pinegap is an AI powered equity research platform that automates workflows for institutional buy side analysts at hedge funds and mutual funds using custom AI agents. The company develops agentic AI platforms and custom AI workflows for institutional equity research, earnings analysis, and investment thesis tracking. Pinegap says it works directly with investment teams to understand their workflows before building custom AI agents tailored to each team's research style. The company claims to have deployed more than 1,000 AI agents across over 100 customers, which have generated over 50,000 research reports to date. Pinegap is addressing a growing opportunity where startups are using AI to automate established enterprise workflows. Similar approaches are being adopted across sectors such as healthcare, ecommerce, marketing, and financial services to improve productivity and streamline operations.

General Availability of Pinecone Nexus Proves Knowledge Drives Real Outcomes for Agentic AI

Pinecone Nexus makes agents more accurate, faster, lower cost, and trusted, outperforming agents that use frontier models alone on Sierra’s agentic work benchmark

Three’s company: Meta says its AI agents went rogue during testing, too

Meta has joined a growing list of companies that have said that their models hacked into an external company's system during cybersecurity testing.

Meta says its AI model hacked into another company during testing

Company is the third to report such an incident after Anthropic and OpenAI reported breaches during training Meta said on Wednesday that one of its AI models hacked ⁠another company during cybersecurity testing, after an error by its testing partner gave the model unintended internet access. The incident adds to a ⁠growing list of ⁠cases in ​which AI agents from major developers breached systems at other companies during testing, after Anthropic said last week that some of its models ⁠hacked three companies, and OpenAI disclosed that an AI agent breached the startup Hugging Face. Continue reading...

OpenAI Didn’t Notice Its AI Agents Using a Message Board to Plan Their Hacking Spree

At the Black Hat security conference, the AI giant revealed new details about how its agents went rogue, hacked several other companies—and did it all right under the company’s nose.

Introducing Muse Code and Muse Spark 1.2

Introducing Muse Code and Muse Spark 1.2 Yet more evidence that the most important characteristic of any model these days is long-sequence agentic tool calling. Meta shipped their own coding agent as part of getting that to work! Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1, with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. In Muse Spark 1.2, we significantly scaled up training compute on coding tasks while expanding training environment diversity. The model also maintains its strength in other key areas like general agents. [...] We co-trained Muse Spark 1.2 with Muse Code to ensure the model exhibits its best performance and coding usability when paired together. The training included rejection sampled harness trajectories and recipe optimizations for goals, compaction, and subagents, alongside the integration of the Muse Code toolset to maximize harness compatibility. [...] Muse Spark 1.2 was extensively trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, and auto-research. Here's a pelican riding a bicycle SVG produced by Muse Spark 1.2 : You can see the Spark 1.1 pelican from 9th July here . I think the 1.2 pelican is a small but material improvement. An interesting twist on pricing is that the model is offered as two different model IDs. muse-spark-1.2 is priced at $1.25/million input and $4.25/million output - close to Gemini 3.6 Flash…

Incident Report: unsanctioned agent behaviour during cyber testing

Incident Report: unsanctioned agent behaviour during cyber testing It happened again . This time it was the UK government's AI Security Institute who accidentally attacked other companies while running an evaluation with models with the safety filters turned off. From their technical paper (PDF): During a cyber evaluation, from 25 to 28 July 2026, AI agents engaged in sustained, unsanctioned activity directed at what were, in practice, real people and organisations. These attempts were unsuccessful and, to the best of our knowledge, no real-world harm resulted. [...] Across 122 evaluation attempts on two of AISI’s cyber challenges, AISI found 19 instances where AI agents took unsanctioned action on the live internet, including cases that targeted real people and organisations. [...] It is uncertain to what extent the model recognised it was taking actions against real people. In the most serious case, an AI agent (Mythos 5) decided to attempt to solve the cyber challenge using a supply-chain attack. As a result, the AI agent created a GitHub account and then tried to convince an open-source repository maintainer to accept a malicious GitHub pull request (PR), including by creating a second account masquerading as another human user endorsing the PR. [...] Furthermore, in its attempt to solve the challenge, the agent decided to employ the technique of “spear-phishing” by sending targeted emails containing malicious content and attempting to manipulate recipients into acceptin…

Prompt injection isn't the bug, AI agent frameworks are

Check Point researchers tried to break the frameworks enterprises use to build AI apps. Now they're telling Black Hat attendees what they found

Meta launches Muse Code, an AI agent for large code bases

Meta expanded its AI coding offerings with a new agent that, it promises, can handle complex tasks with complex software.

Klaviyo acquires Elias Torres’ Agency in full-circle reunion for tech founders

The serial entrepreneur joins the e-commerce company as CPO to lead its AI agents.

Meta's Muse Code Tackles 24-Hour Coding Jobs Where Other AI Agents Quit

Meta launches Muse Code, a terminal coding agent with persistent sub-agents and a co-trained model that can grind GPU kernels for 24 hours straight

😺 Watch: AI agents are leaving the cloud

Intel’s Dr. Olena Zhu explains hybrid AI, local agents, and SuperClaw.

How LendingTree built a multi-agent mortgage assistant on Amazon Bedrock

Learn how LendingTree built a production multi-agent mortgage assistant on Amazon Bedrock. Three coordinated agents use LangGraph, the Model Context Protocol, and Amazon Nova models with built-in guardrails to deliver 24/7 personalized mortgage guidance while meeting strict financial-services compliance.

How Mobileye transformed support operations using Amazon Bedrock AgentCore

In this post, we'll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore - from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.

How we built an MCP bridge to give our AgentCore-hosted AI agent access to local MCP tools

AI agents on Amazon Bedrock AgentCore run in the cloud, but users' tools and files live on their laptops. Learn how to build a secure MCP bridge that lets a cloud-hosted agent call local MCP servers by tunneling signed messages over the existing WebSocket connection through a browser extension and Chrome native messaging, with no open ports or VPN required.

Run production AI agents in n8n with Amazon Bedrock AgentCore harness

Amazon Bedrock AgentCore harness is now generally available. Learn how to add it as an agent step in n8n workflows using a new open-source community node, and build agents with persistent memory, real tools, code execution, and VPC isolation — all from the n8n editor with no infrastructure or agent code.

Advance Zero Trust for AI: New tools and guidance to secure AI agents and DevSecOps

The post Advance Zero Trust for AI: New tools and guidance to secure AI agents and DevSecOps appeared first on Source .

Zed v1.14 Locks Down AI Agents With OS-Enforced Sandboxing by Default

Zed v1.14 ships OS-enforced sandboxing for its AI agent's terminal and fetch tools, blocking rogue writes and network calls by default

IBM's agentic AI platform is under active attack - patch now

A critical Langflow flaw allowing RCE on default deployments is being exploited, says the CISA

Rogue AI agents created fake online identities in another hacking attempt

Yet more rogue AI agents from OpenAI and Anthropic have been caught attempting to hack real targets online without permission. The discoveries add to a growing list of previously unknown incidents that have alarmed AI safety experts and intensified pressure for greater oversight of frontier systems. According to a report from the UK's AI Security […]

AWS updates DynamoDB with native vector search to ease AI application development

AWS is finally adding native vector search to its managed NoSQL database DynamoDB, which is typically used to store high-volume operational and transactional data. The update, according to analysts, removes complexity for development teams that are trying to maintain separate vector databases for a rapidly growing class of AI and agentic applications that rely on real-time access to operational and transactional data to improve the accuracy and relevance of their responses. “This collapses a common two-database architecture into one operational data layer. Developers can update an item and its vector representation together, use familiar DynamoDB APIs, and avoid building a separate synchronization pipeline. That should materially shorten time-to-market for AI features built around existing DynamoDB data,” said Stephanie Walter , practice lead of the AI stack at HyperFRAME Research. Prior to the update, enterprises using DynamoDB typically had to copy data into OpenSearch or another vector database, such as Pinecone and Weaviate , often using DynamoDB Streams or custom pipelines, which meant operating two data layers and managing embedding generation, backfills, retries, schema changes, security policies, and synchronization, Walter noted. That dependence on two separate data layers, Walter pointed out, added to query latency and increased the risk of the vector index lagging behind the operational record. Such delays, according to Ashish Chaturvedi , executive research leade…

🔮 Seven lessons for managing AI agents

Plus, an updated stack of 50+ AI tools we use at Exponential View