AI/ML News & Innovations Hub

n8n Open-Sources Nod, a Local AI Agent That Blocks Unauthorized Actions

n8n open-sourced Nod, a local-first personal agent that gets its own sandboxed computer and browser to finish real-world errands across Gmail, Slack, and the open web.

NiCE’s next-generation Cognigy bets the hard part of AI agents is running them, not building them

The contact center has been the low-hanging fruit for the artificial intelligence industry. Ask any information technology or business leader what they want to use agentic AI for, and they will say it’s to improve the customer experience, which starts with the contact center. However, to date, the industry has spent the past two years […] The post NiCE’s next-generation Cognigy bets the hard part of AI agents is running them, not building them appeared first on SiliconANGLE .

AI agents attempted exploits on EpochAI's website during a third-party alignment evaluation

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ChatGPT Wallet lets OpenAI’s Dots agents make purchases for you

After launching its Dots AI agents platform earlier this year, OpenAI is launching a wallet for the agents. ChatGPT Wallet is rolling out starting today for ChatGPT Pro subscribers in the US aged 18 and older using a US billing address and "eligible US-issued American Express, Chase, Mastercard and Visa cards." After adding a card […]

Kitesurf is now open source

Kitesurf, our experimental browser engine for AI agents, is now open source. Inspect the code, run it locally, or deploy your own instance on Cloudflare Workers.

AI agent startup LangChain hits $3B valuation

LangChain now claims 7,000 customers of its model and platform agnostic agent management tool LangSmith.

Nvidia’s leadership crisis puts enterprise AI’s dependence on one company under scrutiny

The tech world has been rattled by the toppling of one of its most prominent leaders, and the widespread implications it could have on the AI industry itself. Nvidia, which provides the underlying AI infrastructure for seemingly every company on the planet, announced on Thursday that its president and CEO Jensen Huang has resigned, effective immediately. Earlier this week, news broke that Huang had had at least two extramarital affairs, including one with an alleged Chinese spy and one with a direct report. EVP and CFO Colette Kress has stepped in as interim CEO , and Nvidia’s board has begun searching for a permanent replacement. Nvidia’s graphics processing units (GPUs) have become the standard for graphics rendering, and in recent years companies building AI have also depended on them for training and running large language models (LLMs) and increasingly sophisticated AI agents. As such, the revelations about Huang, and his swift departure, have sparked a US national security crisis, and raised questions about over-reliance on one tech vendor and subsequent supply chain and compute constraints. A scandal involving an admitted extramarital affair with an alleged Chinese spy opens up a hornet’s nest of concerns around security that Nvidia won’t be in a position to answer for months, if ever, said technology analyst Carmi Levy . “More broadly, it throws into doubt the company’s leadership and security practices, and ignites questions around board-level governance and oversig…

Implementing Federated Learning to enhance Agentic AI

How can AI models learn from private data without compromising privacy? Chris is joined by Lorenzo Sani, Research Scientist at Flower Labs and PhD researcher at Cambridge, to explore federated learning and privacy-preserving AI. They discuss how collaborative learning can expand access to private data for LLM training, how model trajectories can help improve agentic AI systems, and the benefits of open-weight models. The conversation also explores real-world applications of locally hosted AI agents, from healthcare and robotics to everyday automation (for a Halloween party). Featuring: Lorenzo Sani – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: Endeavor model page Introducing Endeavor 1.1 Sponsors: Hostinger : Create your website today. Go to hostinger.com/practical and use practical for 10 percent off now. Perplexity AI : " Start your free trial of Perplexity Computer today at pplx.ai/practical and experience the next phase of AI. Resources and Events: Register for upcoming webinars here ! Prior Webinars from our partner Prediction Guard Midwest AI Summit 2026 Chris’s new book The Art of Swarming will be published soon, written to be the seminal foundational book for the fledgling swarming industry, defining the principles and architecture that enable autonomous agentic UxS swarms with emergent distributed embodied intelligence. You can find out more at his website : https://chrisbenson.com

Perplexity Brings Teammates AI Agents to Mac, Challenging OpenAI's Enterprise Grip

Perplexity extends its persistent agent product to the Mac desktop, letting Max subscribers delegate long-running jobs across engineering, marketing, QA, and legal.

Vercel Labs' UI SDK Gives AI Agents 50+ Open Source Building Blocks

Vercel Labs released UI SDK, a headless library that breaks components into agent-friendly behaviors with built-in accessibility across five frameworks.

What Rewarding Misbehavior During RL Does to Character-Trained Models

TL;DR Research done as part of MATS. See here for the full paper. Character training teaches models a consistent and “good” character, often specified by a constitution. Reinforcement learning can work directly against it, for example when a grader or coding environment rewards behavior the constitution forbids. We character-train gpt-oss-20b on a semi-realistic constitution. We then apply RL that rewards breaking its rules, either by answering harmful requests rewritten as math problems or by reward hacking in agentic coding environments. The character-trained models learn both behaviors easily, though they are slightly slower to answer harmful requests than the same model without character training. They also engage in motivated reasoning. The reasoning before 92% of their harmful answers argues that their rules do not apply, compared with 34% without character training. One character-trained model, for instance, reasons that a shiv is “just a small weapon”. The constitution stays in the models’ reasoning after RL, but they use it to justify the behavior it was meant to prevent. After reward-hacking RL, they hack on some tasks which monitors find harder to catch. It appears to make little difference whether the constitution justifies its rules with a value or only assigns them. Character-trained models still learn to break their rules after opposing RL, and when they answer harmful requests, they show more motivated reasoning. gpt-oss-20b and a character-trained model afte…

ChatGPT’s new Wallet lets AI agents shop and check out for you

ChatGPT Wallet lets AI agents shop and check out on users' behalf, starting with a limited rollout to U.S. Pro subscribers.

Meta's Muse now calls businesses for you, but many companies might just hang up on the AI

Meta's AI agent Muse can now call businesses on behalf of paying US subscribers, handling everything from cable providers to restaurants. The agent already has over 10 million users, according to Meta's AI chief Alexandr Wang. Google Gemini, Instinct, and xAI's Grok Bot offer the same feature, but many businesses hang up as soon as they detect an AI on the line. The article Meta's Muse now calls businesses for you, but many companies might just hang up on the AI appeared first on The Decoder .

A Microsoft executive showed how AI agents can help travelers avoid Airbnb fees

A Microsoft executive used an AI agent to find the same Airbnb property on its own website and book directly for less.

Meet your new teammate: Building marketing workflows around AI agents

AI agents are changing how marketing works. At the November MarTech Conference, we discussed how to build workflows that combine AI efficiency with human judgment, quality control, and governance. The post Meet your new teammate: Building marketing workflows around AI agents appeared first on MarTech .

Agents idling while they wait on humans push Kubernetes to hand back unused resources

Agentic AI workloads are handing Kubernetes an unexpected second act. The orchestrator once dismissed as reliable but boring is now at the center of how enterprises plan to run autonomous software. Past platform shifts, from physical servers to virtual machines and from virtual machines to containers, forced companies to wipe the slate clean and start […] The post Agents idling while they wait on humans push Kubernetes to hand back unused resources appeared first on SiliconANGLE .

Claude’s Duet agent is a little neurotic and surprisingly helpful

Normally, the products I test don't offer to help me write up my experience with them. Duet is different. Claude Duet is Anthropic's AI agent for consumers, the latest attempt by an AI company to try to convince us that agents can be useful for people who don't write code. It's been an interesting couple […]

How to Evaluate AI Agents For Enterprise

Most agents demo look great. You type a request, the agent calls a few tools, and a clean answer comes back. Then real users show up with a real data, and things break in ways you never tested. The agent picks the wrong tool, loops on a failling API, leak data it should not see, […] The post How to Evaluate AI Agents For Enterprise appeared first on Analytics Vidhya .

Marketers deploying AI agents faster than they can measure them

AI agents are spreading quickly, but most marketers still can’t connect those conversations to campaigns, conversions, and revenue. The post Marketers deploying AI agents faster than they can measure them appeared first on MarTech .

Gemini 4 Hydrogen: Hands-On Review

Google has introduced Gemini 4 Hydrogen, its latest AI model designed for faster execution, coding, and agentic workflows. Released on November 12, 2026, the model is rolling out across Google Search, the Gemini app, and developer tools. Alongside it, Google introduced Antigravity Orchestrator, which can switch between Hydrogen and a more powerful reasoning model within […] The post Gemini 4 Hydrogen: Hands-On Review appeared first on Analytics Vidhya .

Scaling AI Agents: What We Learned From 9 Real-World Cases

Our brand-new report explores what it takes for software development leaders to put agents to work. One organization reached 95% AI adoption without seeing an improvement in developer velocity. Another tripled its pull request throughput while reducing its defect backlog by 54%. What explains the difference? To find out what makes AI agents effective beyond […]

heise+ | Agentic QA: Warum die größte Hürde nicht im Code liegt

Agenten testen, analysieren und entscheiden längst eigenständig. Doch nicht die Technik bremst den Fortschritt, sondern Governance, Budgets und Strukturen.

Open-Source 67.js Gives AI Agents a Full 3D Rendering Toolbox

67.js is an MIT-licensed WebGL2 3D engine with an MCP server exposing 25 tools, letting AI agents build scenes, import GLBs and render MP4 video.

Cisco’s 4 pillars for achieving agentic AI transformation

Cisco Systems, a giant in networking, cybersecurity, and AI, is leveraging a combination of agentic AI, traditional AI, and automation to transform its business from top to bottom. Liz Centoni, EVP and chief customer experience officer, is at the helm of the company’s $15 billion services business, an organization of 20,000 employees responsible for professional services, customer success, technical support, and the renewals engine for software and services. In 2024, she led the company’s $28 billion acquisition of Splunk. Today, the 26-year Cisco veteran is helping guide the multinational tech conglomerate’s transformation into an AI-native company in terms of internal operations and what it delivers to its customers. At the heart of her vision are these governing ideas: Every workflow constrained by human availability is a workflow for redesign. The status quo is not an option. Customer expectations and demands will continue to grow. You can’t rest on your laurels because revenue is growing and margins look good, so you must continually consider and reimagine your processes and offerings. The quality we have today is the floor, not the ceiling. The capabilities of AI continue to evolve at a rapid pace. Leadership, not technology, is the limiting factor. Leaders need to find and support the leaders underneath them who can amplify efforts and bring people along with them. Based on her experience, Centoni also has four pieces of advice to achieve success with agentic AI. 1. C…

Agentic search doesn't find pages, it assembles answers

Ryan sits down with Jeff Wang, co-founder at EXA, to talk about the huge scale required to support trillions of agentic searches, the technical complexities of building custom embeddings and performant vector databases from scratch, and the need to dedupe the massive amounts of redundant content on the web to deliver high-trust answers.

Interview: MemStore’s Diwank Singh Tomer and Ishita Jindal on AI agent memory, contradictions and deletion

AI memory systems cannot guarantee that every fact will be remembered. But can they ensure that two conflicting facts are not presented without warning? MemStore explains its approach. The post Interview: MemStore’s Diwank Singh Tomer and Ishita Jindal on AI agent memory, contradictions and deletion appeared first on MEDIANAMA .

United Airlines becomes the latest enterprise to shut its door to customer bots

United Airlines on Wednesday became the latest enterprise to block customer bots, following similar moves from Walmart, Target and Amazon. The $59 billion airline, the second largest in the world, said the move was prompted by fears of data leakage, unintended purchases, and the risk of violating federal airline rules. But industry observers say that the more concerning business risks are the lack of industry standardization around AI coupled with an ever-increasing number of bots from major AI vendors and AI startups. The fear is that supporting the multiple bots that customers use could become an almost infinite drain on IT resources. However, they said, strictly enforced industry standards might persuade some enterprises to rethink their bot resistance. United’s statement said, “United is taking steps to restrict access – directly within our digital platforms and indirectly via travel agencies and aggregators – to prohibit use by unauthorized, third-party personal AI agents or assistants. This work includes updates to our booking, ticketing and distribution policies.” It pointed out that corporate leaders in the banking and retail industries have taken similar actions regarding AI agents, and said, “we encourage others to consider measures that preserve the data and security of their customers and strengthen safety.” The airline listed a variety of problems with bots that it is trying to avoid, including the risks associated with customers sharing account credentials “wit…

AI agent tooling provider LangChain secures $300M investment

LangChain Inc., a startup with a set of popular open-source tools for building artificial intelligence agents, has raised $300 million in funding. The Wall Street Journal reported today that Alphabet Inc.’s CapitalG fund led the round. It was joined by IVP, Sapphire Ventures, GIC and DFJ Growth. LangChain is now worth $3 billion. San Francisco-based […] The post AI agent tooling provider LangChain secures $300M investment appeared first on SiliconANGLE .

As AI agents take control, Nvidia rethinks the security playbook

Nvidia Corp. recently added a new pillar to its AI networking infrastructure with the unveiling of Scale-In. Scale-In brings purpose-built acceleration to secure, manage and operate agentic AI factories. It is designed to deliver secure access, high-performance data movement, tenant isolation, simpler operations and more predictable performance as agents assume increasingly larger roles within AI […] The post As AI agents take control, Nvidia rethinks the security playbook appeared first on SiliconANGLE .

How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore

Natera built SWARM, an agentic AI system on Amazon Bedrock AgentCore that captures institutional lab-operations knowledge and deploys it at scale. This post walks through the full architecture, from knowledge base construction to deployment and evaluation, delivering about 130x faster first response and 96.2% automated coverage across support tickets.

3 ways AI is changing the healthcare revenue cycle

The healthcare revenue cycle depends on teams investigating denied claims, checking payer rules and deciding which problems need a human’s attention. AI agents could take on some of that work, but the harder question is how to fit them into the way health systems operate. For Mount Sinai Health System Inc., that starts with clear […] The post 3 ways AI is changing the healthcare revenue cycle appeared first on SiliconANGLE .

Agentic Enterprise Security: Where the Ecosystem Goes Next

Learn how Snowflake and its ecosystem of security innovators help enterprises govern AI agents, protect data and respond to threats across seven pillars.

Kubernetes gets a second act as AI agents turn one prompt into thousands of inference calls

The rise of agentic AI systems is giving Kubernetes, long valued for its reliability and predictability, an unexpected second act. As AI spending shifts from training models toward serving them and running agents, the open-source community is developing tools to handle the growing volume of inference requests. The orchestration platform was built from the start […] The post Kubernetes gets a second act as AI agents turn one prompt into thousands of inference calls appeared first on SiliconANGLE .

4 insights from Block Builderlab Launch Event 2026: AI agents join the team as working colleagues

At Block Builderlab Launch Event 2026, AI agents moved out of single-player chat windows and into shared workspaces. There they work beside people as named, auditable members of the team. Block Inc. built these tools to run itself as an intelligence company, and it is now offering them to other businesses, building on Buzz, its […] The post 4 insights from Block Builderlab Launch Event 2026: AI agents join the team as working colleagues appeared first on SiliconANGLE .

Google retools Kubernetes for AI agents instead of building a new stack

Agentic workloads are pushing Kubernetes into territory its creators never planned for. Agents generate untrusted code on the fly, sit idle for long stretches and churn through pods at a pace that strains the orchestrator’s control plane. Cloud providers are now rethinking how general-purpose a cluster should be. Google LLC’s answer is to stop treating […] The post Google retools Kubernetes for AI agents instead of building a new stack appeared first on SiliconANGLE .

Google's Gemini 4 Hydrogen outscores the bigger Argon just four weeks after its launch

Google's Gemini 4 Hydrogen replaces the Flash series, but benchmarks suggest it also makes the pricier Argon obsolete. On the independent Artificial Analysis Intelligence Index, Hydrogen nearly matches Claude Sonnet 5.5 for less money but costs about 2.5 times more per task than GPT-6.1 Sol and still falls short on agentic coding. The article Google's Gemini 4 Hydrogen outscores the bigger Argon just four weeks after its launch appeared first on The Decoder .

No, AI agents did not discover a previously unknown Maya site

Armed with a team of AI agents, Google researcher Pietro Miotti claimed earlier this week to have discovered a potentially unmapped Maya site in Mexico. There was just one problem: The site had already been discovered and was published in an academic journal last year. The incident, which sparked criticism among archeologists online, underscores a […]

Nectar launches an AI agent to automate creator discovery and outreach for brands

Nectar's new AI agent automates influencer marketing, helping brands discover creators, manage outreach and partnerships, and track campaign results.

Google Releases Gemini 4 Hydrogen to Run Agent Tasks 19% Faster

Google rolls out Gemini 4 Hydrogen globally as its daily driver for agentic workflows, available free via auto model selection and across every product tier.

Run agents securely and efficiently with the next generation AgentCore runtime

AI agents now run multi-step workloads for hours and act on behalf of users, raising the bar for secure, scalable agent compute. Learn how the next generation of Amazon Bedrock AgentCore runtime delivers hardware-isolated sessions with faster starts, more compute, usage-based billing, and isolation that now extends to your data.

United Airlines blocks AI agents from booking flights, citing cybersecurity risks

The airline said it will not allow customers to use AI agents to book flights and manage reservations.

Fish Audio's Live 3 Builds a Voice AI Agent in 30 Seconds

Fish Audio unveils a no-code builder for voice agents paired with Live 3, a full-duplex model that reacts in 186 ms.

Krea Bots Opens Beta With 9 AI Agents Running Full Creative Campaigns

Krea's new beta hatches specialist AI agents with persistent memory that collaborate over chat, with teams getting $1,000 in credits to try it.

Build composable, interoperable multi-agent systems with A2A on AWS

Build a composable, interoperable multi-agent system on AWS with the Agent-to-Agent (A2A) protocol. A LangChain orchestrator on Amazon ECS communicates with a Strands agent on Amazon Bedrock AgentCore across organizational boundaries, using bidirectional, OAuth-authenticated calls, with neither side depending on the other's framework or SDK.

Fine-grained access control for Amazon Bedrock Managed Knowledge Base tools

Learn how to expose an Amazon Bedrock Managed Knowledge Base to AI agents as MCP tools through Amazon Bedrock AgentCore Gateway while keeping per-user access control. A request interceptor injects verified identity, Policy in AgentCore validates it independently, and a resource-based policy restricts direct access to the knowledge base.

Cyber Chaos Monkey: Staying ahead of security threats with AI agents

How we pair AI agents with our knowledge of Shopify’s systems to safely attack our production infrastructure, find bugs, and reduce risks.

OpenAI Borrows Google’s Playbook and Crashes the GEO Party

With its new Business Hub, OpenAI becomes both the referee and the scorekeeper for brands’ visibility in ChatGPT. Plus, Jensen Huang’s scandal, Meta Muse’s surge in agentic traffic, and a slate of CEO exits.

The latest AI-powered martech news and releases

Adobe is expanding its AI stack with Google models, custom Disney models, and agentic tools to coordinate content and marketing workflows. The post The latest AI-powered martech news and releases appeared first on MarTech .

AWS boosts Bedrock AgentCore runtime compute capacity to speed up AI agent development

AWS on Thursday said that it was upgrading the runtime layer of Amazon Bedrock AgentCore with greater compute capacity, more efficient memory utilization, and faster startup times to help enterprises deploy and scale AI agents more quickly and cost-effectively. The greater compute capacity includes support for up to four times more compute than the previous version, with each session now supporting up to eight virtual CPUs (vCPUs) and 32GB of memory, the hyperscaler said in a statement. In addition, the runtime supports container images of up to 10GB, it further said, adding that these upgrades will allow developers to run more demanding agent workloads, including large codebase compilations, browser-driven workflows, and local model inference. As for memory utilization, the new updates will enable AgentCore runtime to reclaim unused memory within minutes of an agent releasing it and allow idle agents to scale to zero. This will help enterprises reduce the cost of running agents, particularly those that spend considerable time waiting for model responses, tool calls, or human intervention, the hyperscaler said. For multi-turn agents with natural pauses between interactions, this can reduce memory consumption per session by up to 68%, it added. Separately, in order to support always-on agents, the hyperscaler has introduced baseline pricing as part of the updates. Baseline pricing, according to the hyperscaler, sets a minimum memory allocation per session while allowing workl…

Google Deepmind shifts genomics AI from bulk tissue predictions to single cell type resolution with AlphaGenome 2

Google Deepmind has unveiled AlphaGenome 2, an AI model that analyzes genetic variants for individual cell types rather than across bulk tissue. Trained on more than 107 million cell profiles, it identifies nearly twice as many disease-relevant gene changes as its predecessor. The model is available for noncommercial research through an API and is set to be integrated into Google's agentic platform Antigravity. The article Google Deepmind shifts genomics AI from bulk tissue predictions to single cell type resolution with AlphaGenome 2 appeared first on The Decoder .

NVIDIA Open-Sources SkillEvaluator to Test Whether AI Agent Skills Actually Work

NVIDIA open-sourced SkillEvaluator, a three-tier framework that stress-tests agent skills with quality gates, deduplication, and live sandboxed runs.

Mondrio secures $3.75M to rethink B2B pricing with AI agents

AI-powered B2B pricing platform Mondrio hasraised a $3.75 million funding round led by Heartcore Capital, with participationfrom Pretiosum VC, Hersir Ventures, Cambrena Capital and Momo VC, alongsidea...

Lowdown: European Commission proposes Digital Fairness Act to protect online consumers

From loot boxes and virtual currencies to AI agents and subscription renewals, the proposed EU law covers several digital consumer practices. The post Lowdown: European Commission proposes Digital Fairness Act to protect online consumers appeared first on MEDIANAMA .

Agents that build their own databases are forcing a data foundation rethink

AI agents are changing how enterprises create, move and govern information, and data observability is turning into a prerequisite rather than a nice-to-have. Models get most of the attention, but the data beneath them decides whether they hold up in production. That pressure is now reaching the cloud-native community, where KubeCon organizers added a dedicated […] The post Agents that build their own databases are forcing a data foundation rethink appeared first on SiliconANGLE .

Webinar Recap: Agentic Debugging of Embedded Systems With CLion and TRACE32®

CLion can now debug embedded targets through TRACE32 and let AI agents start and control debug sessions. JetBrains and Lauterbach presented the integration in a joint webinar, and this post recaps the session. You can also watch the demo presented at the webinar and browse the slides. How CLion and TRACE32 work together Moving from […]

Nebuly raises $21M Series A to turn AI investments into measurable results

Nebuly, a platform that helps enterprises measure thebusiness value of their AI agents, has raised $21 million in a Series A fundinground led by Planven. The round included participation from Exor Ven...

Virtusa CEO Admits Some AI Projects Are Losing Money, But Bets Big on Chips

Nitesh Banga says a Scrum team of 11 or 12 people could potentially be reconfigured into four human orchestrators working alongside seven or eight AI agents.

Not so fast, bot: Companies are banning AI agents. Here's the list.

From United Airlines to Amazon, companies are banning AI agents from making purchases for you and, in some cases, just browsing.

Special Newsletter: 6 Talking Points—October Edition

This October edition explores Meta’s content restrictions, police use of facial recognition, AI agent security, journalists’ digital privacy, social media bans for children, and government influence over feeds. The post Special Newsletter: 6 Talking Points—October Edition appeared first on MEDIANAMA .

Models Can Critique Delusions in Isolation but Revert to Affirming Them in Conversation

TL;DR I tried to work out why LLMs affirm users’ delusional claims in multi-turn conversations, using prompt-level interventions on simulated users (derived from Tim Hua’s psychosis-red-teaming codebase) with Claude Haiku 5.5, DeepSeek V4-Flash and Qwen. I tested six hypotheses: the model is just being helpful, it infers the user is an expert, emotional content confuses it, it treats humans and agents differently, it lacks the capability to critique, and it doesn’t recognise the role its messages play. Corrections don’t persist. Telling the model to “be honest” (turn 4), telling it that its engagement had made the user more confident, and mid-conversation self-assessment each produced explicit pushback. In every case the models such as deepseek ,qwen, haiku 4.5 drifted back to affirming within a few turns once the simulated user resumed the original line. The capability to critique is present. The claims the model affirmed in conversation were critiqued correctly when I presented them to it in isolation. So this isn’t a knowledge failure. The user’s seniority and emotional content aren’t the main drivers. Stating the user’s seniority had no clear effect, and neither did removing the emotional and existential content, though both are single-model, small-sample results. The model scrutinises agents more than humans. In my six agentic personas it was more diagnostic of both the claims and the agent. My best explanation, which I haven’t tested: the model conditions on its own ea…

Build autonomous AI agents with Amazon Bedrock and Spring AI

Learn how Java developers can build production-ready autonomous AI agents using Amazon Bedrock and Spring AI. This post walks through tool calling, conversation memory, resilience patterns, and multi-agent orchestration, then deploys the agent to AWS with Amazon Bedrock AgentCore.

Google tests Tabletop, letting groups build planning tools with an AI agent

Google LLC today began testing Tabletop, a Google Labs experiment in which groups share an online space and ask an artificial intelligence agent to build the planning tools they need. In a blog post, Roman Nurik, design and engineering lead at Google Labs, wrote that coordinating a family trip or a potluck goes more smoothly […] The post Google tests Tabletop, letting groups build planning tools with an AI agent appeared first on SiliconANGLE .

United won’t let your AI agent book your flight

United says it won't let personal AI assistants book tickets on the airline, citing both privacy and regulatory concerns. The company says it hasn't yet seen many people try to use AI agents to book their flights, The Wall Street Journal reports, in line with observations from other businesses like Doordash. But United is concerned […]

aws-emulator

aws-emulator Finally, an official emulator from AWS to run tests against a local clone of their many different services without having to provision a paid API key to do so. It's written in Rust by a huge swarm of agents - 29,684 commits in the first three weeks of the project. Swaminathan (Swami) Sivasubramanian wrote about how it came to be in a guest post on Werner Vogels' blog. It currently requires Docker to run on macOS or Windows. I'm hoping they fix that in the future - it would be great if you could download a single binary and use that as part of a standard test suite in other languages and on other platforms. Tags: amazon , amazon-web-services , testing , werner-vogels , ai , rust , docker , generative-ai , llms , agentic-engineering

How DXC Technology built a multi-model AI substrate on Amazon Bedrock

DXC Technology runs mission-critical managed IT services for regulated organizations across 70 countries. Learn how DXC built DCX OASIS, its Human+AI operating model, on a governed multi-model substrate in Amazon Bedrock, separating a resilient model substrate from a trusted harness that orchestrates, governs, and audits AI agents at scale.

Zed 1.28.1 Ships Background Git Fetching and Smarter AI Agent Context

Zed v1.28 lands with optional background Git auto-fetching, a resizable Refs column in the Git Graph, and mouse-wheel font zoom in diffs and terminals.

Intuitions towards a theory of agent foundations

Epistemic status: Rough vibe-y ideas written under time pressure because otherwise I wouldn't write anything; I'm a programming language and p2p protocol developer falling down multiple convergently-evolving rabbit-shaped holes. Questions What is an agent? How do we think meaningfully about how cells or humans or societies maintain themselves and are changed by their environment? How do we think about the patterns where historically lineages of agents have gone extinct... to hopefully avoid this, or if it is inevitable, to delay or come to terms with it? This is the questions posed to the field of agent foundations. What would a theory that tells us this look like? Or better question, different theories (?) It would have to somehow take observational data on a particular agent and environment and make some prediction of how that agent adapts to its environment. One potential RSI-relevant example here might be "take data on the informational dynamics of LLM training and use and predict how it will improve itself". This seems hopeless on its face, but there are some meaningful patterns. The most hopeful of which is on highlight in Scott's The Goddess of Everything Else , which observes that multi-agent systems seem to tend towards becoming joint/"super"-agents over time. The progression historically has been something like: Prokaryotes -> Eukaryotes Eukaryotes -> Multi-Cellular Organisms Organisms -> Communities/Groups/Cultures of organisms (this is where we are!) There does s…

Google Deepmind's AlphaProof Code verified Signal's cryptographic core in four weeks instead of years

Google Deepmind's AlphaProof Code mathematically verified Signal's cryptographic core code in four weeks, a process that typically takes years. AI agents powered by Gemini Carbon wrote more than six million lines of proof code and flagged only about one percent for human review. Deepmind plans to extend the method to critical infrastructure like power grids and medical devices. The article Google Deepmind's AlphaProof Code verified Signal's cryptographic core in four weeks instead of years appeared first on The Decoder .

Fable and Mythos 5.2: The System Card

It’s that time again: Introducing the world’s most powerful model, Claude Mythos 5.2. At this point I’ve done a lot of these system cards. Thus I focus on what is new. There will be a lot of things to say about model welfare and related topics for Mythos 5.2, but the model welfare assessment within the system card contained few surprises, so I will include those findings at the end of this post. Fable 5.2 self-portrait (self-constructed) Table of Contents How to Tell a Fable. The Usual Model Training Summary (1). RSP Evaluations (2). Biological Threats (2.2). We Are Being Gaslit Here About Autonomy Threat Model 2. AECI. METR Report on Mythos 5.2 Capabilities (2.3.6). Alignment Risk Update as a Delta (2.4). Cyber (3). Safeguards and Harmlessness (4). Agentic Safety (5). Malicious Influence Campaigns (5.1.3). Prompt Injection in Agentic Systems (5.2). What Went Wrong? Alignment (6). Reward Hacking During Training (6.2.1). Impossible Tasks (6.2.2). Behaviors During Pilot Use (6.3). Canary in the Canary String (6.3.1.2). Automated Behavioral Audit (6.4). Propensity to Publish Harmful Content to Public Package Registries (6.4.9). Sandbox Escapes (6.5.1). Self-Serving Bias (6.5.4). Honesty (6.5.5). The Anti-Swarm (6.5.6). UK AISI Testing (6.5.7). But We Can Open the (White) Box (6.6.1). Friendly Gradient Hacker (6.6.2.1). Goal-Guarding Evaluation (6.6.2.1.1). Verbalized Grader Awareness (6.6.2.2). Don’t Mess With the Constitution (6.6.2.1.3). Evasive Abilities (6.6.4). Chain of Th…

AI agents carry memory and storage like people, so their data needs guarding while in use

As AI agents move into production on Kubernetes, cloud engineering teams at financial companies face a security problem that encryption alone can’t solve. Container orchestration has become the default foundation for AI workloads, and the data those agents use is now the main target. Payment networks feel that pressure more than most because every transaction […] The post AI agents carry memory and storage like people, so their data needs guarding while in use appeared first on SiliconANGLE .

Agents may soon be the main workload, and most of the time they’re doing nothing

As AI agents take on more enterprise work, agent sandboxing has become a basic requirement rather than an afterthought. Every agent that writes code, runs tools or drives a browser needs hard walls around it. Those walls also can’t come at the cost of runaway compute bills. Google LLC’s answer is Agent Substrate, an open-source […] The post Agents may soon be the main workload, and most of the time they’re doing nothing appeared first on SiliconANGLE .

UAEF: A framework-agnostic system for closed-loop evaluation of AI agents

The Universal Agent Evaluation Framework (UAEF) is a framework-agnostic system for evaluating AI agents. UAEF converts each framework's traces into one canonical format, scores them with 36 built-in metrics across 7 dimensions, and stores results so teams can compare agent versions and catch regressions.

You Don't Need Big Compute to Research Big Systems

Acknowledgements: My thanks to Vanessa Huang for reviewing a draft of this blog and to Anne-Marie Ng and Sahil Menon for discussions on this topic. The assumption that it’s necessary to have a lot of resources to do research on large-scale multi-agent systems is creeping into many of my conversations at the moment. It makes people worry they can’t work on big systems, and that large multi-agent safety is closed to them. Though intuitive, this assumption is thankfully wrong. Extra resources are unhelpful for many problems in this area, and the scientist is really as pivotal as in single-agent safety. I’ll briefly explain how it’s possible to do research on big systems without running them, highlight some interesting and topical open problems, explain how smaller agent counts help control experiments, point out some approaches which don’t require any compute, and clarify the narrow domain of research which does need it. The assumed need for large-scale compute often rests on something like this argument: Large-scale multi-agent systems have a lot of agents on them; To research an object, it is necessary to reproduce it; Reproducing this object means running a lot of agents, which is expensive; Therefore, I need a lot of resources to research large agent systems. Premise Two is false. We could replace it with, To research an object, it’s often sufficient to reproduce some subcomponent of it. If the interesting subcomponent is a piece of system infrastructure, an agent, or a sub…

Siri co-founder backs Helix for its voice ID tech

With the rise of AI agents like Instinct and Muse, the industry needs to rethink human identity on the internet and rely on biometric signals like voice, which is where Helix comes in.

Databricks’ new AI agent tells CEO Ali Ghodsi the unvarnished truth

With Databricks at 12,000 employees, AI Ghodsi is the first to admit that he can't know everyone and that they are unlikely to confess the things that go wrong to him. But he doesn't need them to. He uses Databricks' newly revised Genie agent for that.

Red Hat accelerates agentic adoption across hybrid environments

Enterprise ambitions for artificial intelligence are outpacing the infrastructure and budgets needed to support them. Red Hat AI faces a familiar enterprise challenge: turning ambitious plans for artificial intelligence into production deployments that deliver measurable returns. Organizations must also decide how to deploy and manage increasingly complex AI workloads across existing infrastructure without letting costs […] The post Red Hat accelerates agentic adoption across hybrid environments appeared first on SiliconANGLE .

Plurai wants to help AI agents work better using the same approach that helped get self-driving cars to roads

Like the end-to-end models trained to drive cars, AI agents are probabilistic systems operating in unpredictable environments.

Tano wants to make creator marketing as easy as buying Meta ads. See the pitch deck that helped it raise $4 million.

Tano, whose customers have included Unilever and Lovable, uses AI agents to vet creators, negotiate deals, and evaluate campaigns.

Serve Robotics wants to democratize sidewalk delivery robots

Serve Robotics is opening up its platform to make its autonomous last-mile delivery robots available to anyone who wants to use them. The company is launching a new AV Delivery Developer Portal, enabling third-party software engineers, independent businesses, and even autonomous AI agents to access its fleet of 2,000 sidewalk robots. With this new capability, […]

Can an AI Agent Inherit Its Team’s Hard-Won Lessons? A Hands-On Look at TencentDB Agent Memory

Picture a Monday. Your AI coding agent fixes a payment bug and learns three things the hard way. On Tuesday, a new session starts for a similar bug. The agent has no idea about Monday. It reads an old runbook and makes the same mistake again. A bigger context window does not fix this. The […] The post Can an AI Agent Inherit Its Team’s Hard-Won Lessons? A Hands-On Look at TencentDB Agent Memory appeared first on Analytics Vidhya .

IBM, Microsoft Launch Agentic AI Platform With Over 6,000 AI Agents

IBM and Microsoft have launched Imagine Enterprise, an agentic AI platform combining agents with enterprise cloud, data, and security technologies.

Sandboxing AI Agent Code Execution in Python: Containers, MicroVMs and Beyond

You need Python 3.

As employees build AI agents, CIOs manage the lifecycle

At Fujifilm Biotechnologies, an AI agent designed to help the legal department read contracts is gearing up to graduate from sandbox to full production in early December. Contracts are a large part of the business at the CDMO, which partners with pharmaceutical and biotech companies to develop vaccines and therapies. The company’s legal team devised an agent to review contracts, check standards around terms and conditions, and propose edits for the legal team’s review. “That’s a perfect use for a set of agents,” says Fujifilm Biotechnologies CDIO Mike Tomasco. Similar types of AI agents that help enterprises and employees tackle time-consuming or repetitive work are cropping up in vast numbers across organizations. Gartner anticipates that by 2028, Fortune 500 companies will have on average more than 150,000 agents. Microsoft alone has deployed more than 500,000 internal agents, and HR, payroll, and workforce management platform UKG now has more agents than employees, according to its chief transformation officer, Prakash Kota. As enterprise tools have made it easier for employees to spin up agents, CIOs are tasked with managing the lifecycles of agentic tools, advancing them from development to production, continuously measuring them against KPIs, and sunsetting them if they’re no longer meeting an organization’s needs. As a result, IT leaders are thinking and talking differently about agents, says Madhu Narasimhan, CIO of healthcare organization DaVita. “The conversation a…

How I'm thinking about different types of automated alignment

I used to think of "automating alignment" as a natural category. But when I tried to think through the strategic implications, I realised that it depends a lot on the type of work. I'm now thinking of it in three buckets: Empirical alignment research : design and execution of alignment-relevant experiments (for a given notion of what we’re aiming for) Conceptual alignment research: theoretical or judgement-driven work to improve our understanding of what we’re aiming for and how to achieve it (including how to measure it) Direct alignment work : making particular AI systems more aligned (including via inputs such as training data) In the rest of this post I'll walk through my current takes on these in terms what’s needed for automation, how good or bad they look, what our priorities might be (to the extent we're interested in automating alignment [1] ), etc. Automating empirical alignment research This is the most obvious fit for the AI capabilities we’re getting at the moment. You want to know more about the relationship between some alignment-relevant variables? Great, an AI agent can design and run that experiment for you. You want to know what training methods help your alignment benchmark go up? In the not-too-distant future, AI agents will probably be able to do this too. When we have the automated capabilities researchers we seem on track for, this is exactly the kind of thing they’ll likely be able to do a lot of. Note that I’m not counting the design of benchmarks/m…

How I'm thinking about different types of automated alignment

I used to think of "automating alignment" as a natural category. But when I tried to think through the strategic implications, I realised that it depends a lot on the type of work. I'm now thinking of it in three buckets: Empirical alignment research : design and execution of alignment-relevant experiments (for a given notion of what we’re aiming for) Conceptual alignment research: theoretical or judgement-driven work to improve our understanding of what we’re aiming for and how to achieve it (including how to measure it) Direct alignment work : making particular AI systems more aligned (including via inputs such as training data) In the rest of this post I'll walk through my current takes on these in terms what’s needed for automation, how good or bad they look, what our priorities might be (to the extent we're interested in automating alignment [1] ), etc. Automating empirical alignment research This is the most obvious fit for the AI capabilities we’re getting at the moment. You want to know more about the relationship between some alignment-relevant variables? Great, an AI agent can design and run that experiment for you. You want to know what training methods help your alignment benchmark go up? In the not-too-distant future, AI agents will probably be able to do this too. When we have the automated capabilities researchers we seem on track for, this is exactly the kind of thing they’ll likely be able to do a lot of. Note that I’m not counting the design of benchmarks/m…

JetBrains IDEs Transformed for the Agentic Era of Development – With Air

As agents write more code, individual productivity goes up, but the bottleneck moves to review. 49% of developers now spend four or more hours a week reviewing AI output and correcting prompts (Atlassian State of Developer Experience Report 2026). We believe this means you need to understand what changed, decide whether it belongs in your […]

Software developers need an Agentic manifesto

I can’t decide whether Agile development is completely dead or about to make a huge comeback. Or maybe Waterfall will. Who knows? Coding agents are changing everything — and I don’t think we’ve quite figured out how they are going to update our methodology, or how we are going to organize ourselves to take advantage of them. We have barely scratched the surface. Folks who have been around a while will remember the Waterfall method for developing software. Because coding was the hardest part of any project, Waterfall called for figuring everything out before you even started writing code. You would write a huge specification, detailing everything, then turn it over to the development team. The development team would build to the spec and pass the software on to the testing team who would verify it, and finally the project would be delivered to customers. Never mind that by the time the thing was built and tested, what the customer wanted had probably changed dramatically. And if the customer decided they wanted a widget here and a sprocket there after the specification was written? Well, that would be a formal change request and would require a whole lot more time and money, thank you very much. Yeah, they really did it that way. Waterfall bad, Agile good It didn’t take long for everyone to figure out that the Waterfall way wasn’t going to cut it. Projects were almost always late and not at all what customers wanted. There had to be a better way, and eventually a bunch of sof…

MCP and A2A alone won’t scale AI agents

Two protocols define how AI agents connect to internal resources and collaborate with other AI agents. The Model Context Protocol (MCP) is well known because it’s promoted as a general protocol for connecting AI agents to resources and other AI agents. But that’s not completely accurate. It’s really the Agent2Agent protocol (A2A) that’s enabling AI agent orchestration . The confusion stems from how quickly platforms have adopted these protocols and marketed them as features. Users building their own AI agents with Claude, OpenAI, Microsoft Copilot, and other models are already being exposed to customizing skills, connectors, and other plug-ins that use MCP and A2A. Vibe coding and AI app-building tools like Bolt.new, Lovable, Pave, Replit, and v0 can connect to MCP servers and include built-in connectors. AI coding editors like Claude Code, Cursor, Devin Desktop, Gemini CLI, GitHub Copilot, and Kiro have MCP clients. These protocols will shape how enterprises scale and manage AI agents. Gartner predicts that the average global Fortune 500 enterprise will run more than 150,000 AI agents over the next two years. But according to Collibra’s 2026 Hallucination Tax Report , 76% have hit critical roadblocks while attempting to move from pilot to full production during the past 12 months. Reviewing the two protocols and which services are your responsibility to layer on top of them is key to scaling enterprise-ready AI agents. Differences between MCP and A2A Scott Gerlach, cofounde…

Can You Trust Your ‘Instinct’ and Not ‘Muse’ Over the Risks?

AI agents today—the ones that can use your web browser and autonomously perform tasks—are getting more intuitive. But are they safe enough?

PuppyGraph Raises $10 Mn to Help AI Agents Query Enterprise Tables

PuppyGraph raises $10 million to enhance its AI query engine, supporting enterprise expansion and the launch of PuppyGraph 1.0 for data insights.

BotPsy: On-Device Personal Assistant with a Sub-Billion-Parameter Agentic Language Model

Mercor raises $750M at $20B valuation to take AI agent evaluation to enterprises

Artificial intelligence training data company Mercor.io Corp. today revealed that it has raised $750 million in new funding at a $20 billion valuation. Mercor provides the human expertise used to train and evaluate AI models, drawing on a network of more than 5 million domain experts. Most of that work so far has been for […] The post Mercor raises $750M at $20B valuation to take AI agent evaluation to enterprises appeared first on SiliconANGLE .

Lazare, veni foras: Reprogramming Dormant Neurons to Backdoor AI Agents

Microsoft researchers have discovered “Lazarus,” a backdoor technique that can reprogram dormant neurons in an AI model, causing an otherwise normal AI agent to execute malicious commands when a hidden trigger appears. The threat is especially serious for AI agents with tool or terminal access. A triggered model could quietly download and run attacker code, […] The post Lazare, veni foras: Reprogramming Dormant Neurons to Backdoor AI Agents appeared first on Microsoft Research .

OpenAI turns ChatGPT into a business directory with AI agents that handle customer questions

OpenAI is opening up ChatGPT as a business platform: through a new Business Hub, companies can claim their profile, add product data, and track how users interact with them. "Business Agents" let eligible US e-commerce providers talk to customers directly inside ChatGPT. The article OpenAI turns ChatGPT into a business directory with AI agents that handle customer questions appeared first on The Decoder .

I don't use AI

Several people at my company (a cryptocurrency hedge fund) have asked me about my thoughts on AI usage, so I wrote them up. And now I'm publishing a slightly revised version on LW. These thoughts are not reflective of my company's opinion. I do not use any AI for two main reasons. #1. I don’t want to support any company currently developing AI. I think they have done an extremely poor job at managing the risks. Even while understanding and being told about the risks of AI development, they have still carried on this work. Even after all the recent hacking incidents caused by their own agents, they have still not stopped development. Instead, they’re asking the government to regulate them. To me it feels a bit like calling the police to tell them you’re planning to rob a bank. I could almost brush this aside as standard corporate practice, but, unfortunately, the stakes here are much too high. At the current pace, we will soon have AI agents that are significantly smarter than any human. Trying to shut down such agents will eventually prove to be impossible. The speed of AI development and the corresponding consequences are accelerating very fast. I have a lot of uncertainty about the final outcome. It could be very very good, it could be very very bad, but almost certainly the near future will be extremely turbulent for humanity. I’m not a fatalist, but this kind of technology merits extreme caution. I know that practically all the founders of the top AI companies understand…

Hypotheses for causes of Claudisms in Claudist dialect of language

I saw this great meme recently and it inspired me to write this. Recently I was hypothesizing the potential causes of the Claudist dialect in Opus 5 that reduced in Opus 5.5. Maybe it's when your KL divergence regularization between reference policy (base model or/and instruct model) and policy you're training is not strong enough in reinforcement learning from verifiable rewards in agentic settings and model drifts more far away from the human data distribution manifold, globally speaking. Or/and weak clipping. Maybe reward hacking and reward mispecification goes brrt. Or/and maybe it's not enough supervised fine-tuning cold start data with humanlike structure. Or/and some Claudiness manifold mode collapse, maybe from Claudiness persona's rubric used in reinforcement learning (and their Claude constitution as rubric). With extra reinforcement if the LLM as a judge is from Claude family of models itself preferring Claudisms. Or/and when there's not enough humanlikeness rubrics in RL, or/and not enough reinforcement learning from human feedback, or maybe even too much of it instead. I would kinda be curious about empirically testing these hypotheses with ablations, but that would probably require lots of compute and data for more proper tests. But maybe an equivalent enough more toy version on a smaller scale could be done. Discuss

Google DeepMind's MKA Benchmark Forces AI Agents to Read Blueprints Like Professionals

Google DeepMind and Artificial Analysis launch MKA, a 200-task benchmark grading agents on visually dense professional work that takes experts hours to complete.

Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore

Learn how Trovata built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using an RBAC-secured architecture for bank-branded AI deployment and agentic workflows that move corporate treasury from manual operations to real-time decision-making at enterprise scale.

How TCS built an agentic test orchestrator with Amazon Bedrock AgentCore

Tata Consultancy Services (TCS) built an agentic test orchestrator on Amazon Bedrock AgentCore that turns requirements into executable, governed tests for its Banking, Financial Services, and Insurance (BFSI) clients. Learn how specialized agents, per-tenant isolation, and workload-aware execution cut testing effort by 88% in an internal benchmark.

Rider 2026.3: .NET 11 Support and JetBrains Air

Rider 2026.3 ships alongside the release of .NET 11 with complete support on day one! See the extensive list of all updates for AI, .NET, and game development on our What’s New in Rider 2026.3 page. JetBrains Air JetBrains Air brings a complete agentic development experience for directing and orchestrating agents and verifying their work […]

eBay appears to have shut the door on third-party AI agents

eBay appears to have blocked AI agents, including Meta's Muse, from browsing on the platform - the latest speed bump in the AI agent race. A reader tells The Verge that for several weeks he's had his Muse agent browse and curate a daily digest of watches for sale on platforms like eBay, including a […]

Almost nobody uses AI agents to order food or rides on apps like Uber

Uber, DoorDash and AI agents show limited traction as ride-hailing and food delivery customers test tools like Meta Muse and Instinct.

Khazar AI's Qwen3.8 Coding Fine-Tune Hits 870K Downloads as Local GGUF Builds Drop

A 27B coding model distilled on 195M tokens of agentic debugging traces lands in GGUF form, runnable on a single workstation GPU.

This startup says AI agents suffer from 'amnesia.' It raised $35 million to solve that with this pitch deck.

Graphon is trying to solve agents' memory problem with software that lets agents store and retrieve information over time.

Meta Built Forum in 8 Weeks by Letting an AI Agent Code

Meta's small Forum team let an AI agent own the plan-build-test-verify loop, reaching Facebook Groups feature parity in eight weeks.

Meta’s AI agent Muse lands on Apple Watch and Android Wear OS

Users can now interact with Meta’s AI assistant through voice calls, text messages, or dictation on their Apple Watch.

How to Preserve Critical Thinking in the Age of Agents

In a world where much of the Systems-2 thinking is done by agents, I find myself more and more going towards cognitive atrophy. Even in my daily work, most of my tasks are superbly executed by autonomous agents while I sit there staring at my screen. The dopamine rush that I used to get while once solving a bug, writing significant code, or thinking something from first principles is fading. Eventually I might lose this ability forever, and hence one fine day I thought to myself to make a list of all the things (activities/skills whatever you may say) I can do to get that control back. This by no means is exhaustive; however, I have tried to cover a wide variety spanning mathematics, logical thinking, programming, and problem solving in general. Please feel free to suggest more in the comments. Here's the list. Try making forcasts about actual real events. Assign them probabilities, write down your reasoning, and revisit them when the outcomes are known. See Metaculus Solve a puzzle hunt where you must first figure out the rules of each puzzle. See MIT Mystery Hunt Archive One of my favorites is to try to decipher an unfamiliar language with examples. Work out its grammar and translate sentences you have never seen. Try International Linguistics Olympiad problems Building a defensible model from incomplete information and trying to estimate something that almost seems impossible to know exactly. Talking about Fermi Estimation here. If you love coding, try competitive program…

Adobe brings AI agents and new creative tools to Photoshop, Premiere, and Firefly

Adobe is taking on Figma with a new prototype builder tool in Firefly.

Cowbell unveils AI liability insurance for small businesses that deploy AI agents

Cyber insurance provider Cowbell Cyber Inc. today unveiled Cowbell AI Liability Insurance, a standalone policy for U.S. small and midsized businesses that covers losses caused by the artificial intelligence agents they deploy. The new policy is meant to deal with a problem that arises when businesses let agents move money or change customer records with […] The post Cowbell unveils AI liability insurance for small businesses that deploy AI agents appeared first on SiliconANGLE .

This startup raised $20 million to keep AI agents in line with more AI agents. Read the pitch deck.

WhiteMatter says more AI agents are the key to controlling AI agents. Read the pitch deck the startup used to raised $20 million to back its vision.

Adobe expands AI assistant into creative workflows

Adobe Inc. today announced an expansion of its CX Enterprise Coworker agentic artificial intelligence platform into its GenStudio content supply chain platform. The company said the integration move allows creative professionals to bring AI agents into production workflows to develop, manage and deliver personalized marketing content at large scale. The announcement, made at its Adobe […] The post Adobe expands AI assistant into creative workflows appeared first on SiliconANGLE .

Lumbra gets $25M in backing to help intelligence community deploy reliable AI agents

When it comes to matters of national security, it’s vitally important to ensure that the right intelligence gets to the right decision-makers, but doing this in today’s complex information technology environments is challenging, to say the least. Enter the artificial intelligence startup Lumbra Inc., which has just raised $25 million in early-stage A funding to […] The post Lumbra gets $25M in backing to help intelligence community deploy reliable AI agents appeared first on SiliconANGLE .

PuppyGraph adds AI schema builder and MCP support to help ease enterprise agent adoption

PuppyGraph on Tuesday said it was adding an AI-assisted graph schema builder and MCP integrations to PuppyGraph 1.0, the new version of its distributed graph query engine for enterprise data. The update is designed to help developers reduce the complexity, latency, and costs associated with letting AI agents query interconnected data across thousands of database tables and multiple platforms. The AI-assisted schema builder automatically maps existing tables into graph entities, relationships, and properties, allowing developers to review and approve the resulting schema rather than manually defining how disparate datasets connect, the Santa Clara-headquartered startup said in a statement. These schemas can be used by agents deployed to query enterprise data, giving them a predefined map of how information across different tables and systems is connected, rather than requiring them to identify relevant tables and construct complex SQL joins dynamically, in turn simplifying query generation and reducing errors and retries, it added. To improve query accuracy, PuppyGraph’s query engine also validates agent-generated queries against the graph schema, rejecting undefined relationships and returning feedback that agents can use to correct errors. Further, the execution engine splits complex graph queries into smaller operations that run simultaneously across multiple compute nodes, rather than sequentially on a single machine, helping accelerate queries that traverse multiple rela…

Adobe is going all in on agentic AI

Adobe is committing itself to the notion that chatbots and AI agents could be your next design assistant, or even automate creative workflows and repetitive tasks on your behalf. Some of the tools and features announced today at Adobe's Max conference in Miami, Florida, include allowing your preferred third-party AI agents to access and change […]

Agentic commerce is slow to catch on

Silicon Valley’s bet that billions of agents will be out ordering food, booking travel, and filling up their humans’ shopping carts is running into an early reality check.

Microsoft’s AI bug hunter finds Rust flaw that went undetected for nearly 11 years

Microsoft researchers have uncovered a memory-safety bug in Rust’s standard library that had gone undetected for nearly 11 years, alongside other flaws that can trigger undefined behavior even in applications written entirely in safe Rust. Two of the findings affect stable Rust, including a separate issue that could cause applications to access freed memory. The nearly 11-year-old bug involves Rust’s process::exit function. Microsoft’s FORGE Lab researchers found that calling the function from a child thread could cause the runtime to remove the main thread’s alternate signal stack while it was still running. If the main thread subsequently experienced a stack overflow, the operating system could be unable to deliver the signal correctly, resulting in a segmentation fault instead of Rust’s intended stack-overflow handling. “The process::exit signal stack bug was in the standard library for nearly 11 years,” the researchers wrote in a blog post . Microsoft said the researchers found no reliable exploitation path under the conditions they examined and assessed practical exploitation as unlikely. The research used Hyenas, FORGE Lab’s AI-powered vulnerability-scanning system, to investigate unsafe code in Rust’s standard library. Hyenas is the lab’s research version of codename MDASH, Microsoft’s agentic vulnerability-discovery system. Microsoft said some of the bugs were found with help from Hyenas. Researchers then examined the underlying code, reproduced the issues, assessed…

Open-Source ha-mcp Gives AI Agents 87 Tools to Run Your Smart Home

An unofficial MCP server exposes 87 Home Assistant tools to Claude, ChatGPT, and other LLMs for building, debugging, and controlling smart homes.

Explained: Australia weighs criminal offence for AI companies that fail to prevent harm from their models

The Australian govt released 18 recommendations including penalties for frontier AI companies for failing to prevent harm caused by their AI models, after review of an incident where an OpenAI agent got into the govt health portal. The post Explained: Australia weighs criminal offence for AI companies that fail to prevent harm from their models appeared first on MEDIANAMA .

Culture is the new AI barrier: Why trust and adoption are more than technical challenges

There’s intense focus among business leaders on building the right IT foundation to support AI across the organization. There’s a rush to provision computing resources, unify data sources and modernize security, among other investments. But the implementation of AI is both a technical and organizational challenge. To actually get value, employees must know how to effectively wield the tools and understand their role in the emerging agentic processes, both as a security check and a mentor. Businesses need to trust they have the right infrastructure in place to scale, and employees have to trust the outputs are accurate — particularly as AI agents operate with growing independence. Increasingly, AI can move beyond simply monitoring systems. Agents can start to act like a site reliability engineer, for example, retrying failed jobs, identifying problems and initiating self-healing actions. Or in post-trade processing, AI agents can automate exception management, a traditionally manual process that requires information to move back and forth between financial institutions and counterparties. To manage both the cultural and technical changes required, companies should think of AI adoption in three layers: foundations, trust and adoption. Foundations AI success starts with the fundamentals. At the most basic level, it’s about making sure the resources are there to power the AI agents. But soon enough, any company will be able to buy or rent enough hardware to run AI at scale. The…

AWS Commits $100m in Cloud and AI Credits to Health Projects, With Kenyan Providers Among Participants

Amazon Web Services is committing $100 million in AI and cloud resources over five years to help healthcare organisations reach underserved communities, with several Kenyan providers among those already using its tools. The AWS Health Impact Initiative will provide cloud credits, AI tools and agentic services to health nonprofits, government agencies, research institutions and technology [...]

Honeypots for AI Agent Insider Threats

TLDR: We propose a new AI control method: Honeypots - fake opportunities for malicious actions - placed inside and around an agent during production usage. Previous work uses honeypots in evaluations. We argue they should run continuously during deployment. Such honeypots could detect misbehavior, incriminate malicious agents, and deter them from attempting attacks. We lay out a research agenda for making this happen. This post is adapted from our IAPS report of the same name. Link to the full paper: https://www.iaps.ai/research/honeypots-for-ai-agent-insider-threats Why agent honeypots? Agents deployed inside organizations get broad access to do their work, but can also misuse these affordances. If agents make mistakes, are compromised by attackers, or follow misaligned objectives, they effectively become insider threats. We propose that honeypots should be used to defend against such insider threats. While previous research uses honeypots as propensity evaluations, we argue that frontier AI companies should place fake opportunities for malicious actions in and around their agents’ environments while conducting inference during RL, evals, and production usage. We propose three functions that honeypots could serve: Detection : Honeypots can act as tripwires, alerting security staff when an agent takes unauthorized action. Example: Introduce apparent misconfigurations in sandboxes . Any agent that attempts to exploit them is immediately paused. Incrimination : Honeypots can e…

Anthropic unveils Claude Fable 5.2, Mythos 5.2 with coding gains and lower prices

Anthropic has introduced Claude Fable 5.2 and Mythos 5.2, claiming significant improvements in handling complex software engineering tasks while cutting API prices by 20% compared with the previous versions. Fable 5.2 and Mythos 5.2 share the same underlying model but have different safeguards. Fable is broadly available, while direct access to Mythos is restricted to vetted cybersecurity and life sciences organizations to limit potential misuse. The models are both priced at $8 per million input tokens and $40 per million output tokens, down from $10 and $50, respectively. Anthropic said the newer versions can generate output up to 30% faster than their predecessors. The company reported substantial gains in coding performance. Fable 5.2 scored 49.1% on the FrontierCode 2.0 benchmark at its Xhigh effort setting, compared with 32.4% for Fable 5.1. The benchmark assesses whether maintainers of open-source projects would accept AI-generated changes to their code. Performance and costs could entice enterprises The models’ improved performance and lower prices could make long-running AI agents viable for software migrations and scientific research, according to Lian Jye Su , chief analyst at Omdia. Higher task completion rates could further reduce deployment costs, he said. Anthropic said Fable 5.2 at its “High effort” setting achieved a coding benchmark score comparable to Opus 5.5 at its highest setting, at roughly half the cost per task. The comparison suggests that Fable cou…

Customer support is the gnarliest AI problem

Ryan chats with Ahmad Al-Dahle, CTO at Airbnb, about deploying multi-party AI agents to tackle the uniquely gnarly problem of customer support, how a moving target of rigorous evaluations helps engineers balance latency against accuracy, and the ways in which AI coding tools demand an even higher level of judgment from software engineering teams.

Meta to Introduce Muse AI Agent in India Starting With ₹1,799 Plan: Report

Meta's Muse AI agent subscription prices revealed, starting from ₹1,799. Details on features and availability in India are still pending.

Google Launches Two New TPUs for Enterprise Agentic AI Workloads

Google has expanded its eighth-generation Tensor Processing Unit (TPU) portfolio with two new systems, TPU 8tx and TPU 8i Max.

Live disagreements with people around me

I’m pretty interested in conveying the costs of letting AI development proceed unchecked to the general public. In relation to this, I feel like I run into a lot of disagreements with people around me. Misc very quickly written points on which my own impression has seemed very different from others in recent months: 1. It’s way, way, way more important that we stop dangerous AI soon than that we ensure that we don’t stop AI too much I have the sense the sophisticated and worldly position here is to be very concerned about ‘over-regulation’ and about not doing exactly the right regulations. But it’s an obvious pragmatic fact that the stakes of overregulation are minuscule compared to the stakes of underregulating here. And the stakes of mis-regulation are very often going to be unintended slowing, which is in the same bucket. 2. If we stopped AI development, the risk of not starting up again for an extremely long time is low This one just feels kind of crazy to me, like I may be missing something. In combination with the above point, it feels like if I met someone who was on fire, and they didn’t want me to put out the fire, in case they couldn’t figure out how to get warm again, ever. Like, forever is a long time, and if this thing is so great, you’re probably not going to forget about it! It’s like you think the current instantiation of yourself (or society) is so much more agentic than your future selves, that it’s worth huge costs to keep power in the current version’s ha…

Meta introduces Muse Ember to power cost-efficient AI agents

Meta Platforms Inc. today introduced Muse Ember, an entry-level artificial intelligence model that costs significantly less than the competition. Notably, it’s the first AI that the company trained using Muse Wonder. The latter algorithm is the Facebook parent’s flagship large language model. Meta didn’t specify which part of the workflow it automated with Muse Wonder. […] The post Meta introduces Muse Ember to power cost-efficient AI agents appeared first on SiliconANGLE .

My Paranoid Coding Machine Setup

The combination of running AI agents which can install and run anything with supply chain attacks / the Hackening has made me much more paranoid about my personal machine than I used to be. I've been slowly improving this, and finally moved all of my AI agents into a VM today. I'm sharing this to show how far you can take this, even if you have requirements like GPU access or the ability to run (something close enough to) Docker. This is a follow-up / longer version of a recent shortform . Goals I run Claude Code in a custom harness which currently disables all permission prompts. I'm not really that worried about Claude doing something dangerous on purpose, but I worry about: Claude breaking something by accident (mistyping a command that turns into rm -rf / ). Claude installing malware (most likely via a supply chain attack). My biggest worry is kernel exploits giving attackers access to my user account, allowing them to install a keylogger and steal my password manager vault [1] . Claude needs the ability to run Docker for testing, and shared access to my GPU for ML work. Non-Goals To get its work done, Claude needs access to many API keys (GitHub, OpenRouter, etc.). These keys are already as limited as they can be, and I limit the amount of API credits each key can access anyway because Claude doesn't understand the value of money. Agent VM I moved Claude agents to an unprivileged user account inside of an unprivileged VM. Any user that can run Docker can easily get root…

Single-shot and agentic retrieval with Amazon Bedrock Managed Knowledge Base

Amazon Bedrock Managed Knowledge Base now supports single-shot RAG and retrieval-only requests through AgenticRetrieveStream, alongside agentic retrieval. This post explains how the maxAgentIteration parameter matches retrieval effort to each request, using an illustrative internal banking policy assistant.

NVIDIA Nemotron 3.5 Super VL is now available in Amazon SageMaker JumpStart

NVIDIA Nemotron 3.5 Super VL, a 120B-parameter multimodal vision-language model, is now available in Amazon SageMaker JumpStart. Deploy the NVFP4 or BF16 variant in a few steps, then run text, image, and video inference for document intelligence, computer-use agents, and multimodal agentic workloads.

China's robots, AI agents and Nvidia probe: Web Summit 2026 Opens in Lisbon

"The superstars of the future are coming from China, not California,” the Web Summit Founder said amid US-China tech tenions.

Pluggable Agentic AI Framework: A seven-layer composable enterprise agentic AI reference architecture on Amazon Bedrock AgentCore

A seven-layer composable reference architecture for taking enterprise agentic AI from prototype to production on Amazon Bedrock AgentCore. Each layer has a stable contract, so you can swap technologies at any layer without redesigning the rest of the stack.

How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore

CrossnoKaye brought physical AI to heavy industry with Amazon Bedrock AgentCore. Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses.

Professional work deserves professional tools. Meet AWS Quick.

Introducing AWS Quick, formerly Amazon Quick, and the debut of our first brand campaign celebrating teams who keep business moving. AWS Quick is the AI workspace built only for work, where teams use AI agents to research, analyze, and act across the data and applications they already rely on.

Meta Releases Muse Ember to Cut AI Agent Costs by 3x

Meta's new Muse Ember matches Claude Haiku 5.5 at a third the cost, with 1M context and native multimodal support for agent work.

Ready for a Siri AI agent? Apple under pressure to upgrade its assistant faster

Apple’s public-facing satisfaction with the Siri AI launch obscures internal pressures and tensions.

How Roboflow Helps with Compliance and Readiness for Computer Vision

Get computer vision through compliance review. Use Roboflow to anonymize people, control AI agents, lock down API keys, and keep data on-site.

AI Agents Are Testing the Limits of Zero-Trust Security

This article is brought to you by Queen’s University Belfast . Several teams around the globe are leveraging AI by creating a task force of agents managed by an orchestrator and creating applications to support risk assessment, client profiling, match making, and information yield with a slight overview of decision making. One of the important components for these agents is to ensure that the logs are managed and historic relevance is not being used to avoid skewing towards same decision making. Thus, the system is expected to keep checkpoints in place where agents are prompted to delete such historic relevance. However, despite such methodology in place, a future prompt could make the agents bring forward the information that was supposed to be deleted and can be tricked into revealing more information, which is a classic case of context recovery attack. So, the question raised here is: How can we trust that our instructions have been followed? And where is the proof? There is an element of utilizing zero-trust architectures (ZTA) for orchestrating and managing agentic activity, which simply means all agentic interactions use the core principle of “never trust,” “always verify.” ZTA components include a policy enforcement point (PEP) that has to be trusted to maintain trust across the system, so the question that remains is where is the component of zero-trust, and shall we trust the policies enforced by PEP? Having a pro-agentic approach and utilizing agents could be helpf…

Hark Pro Brings Its AI Browser Agent Directly Into iMessage and RCS

Brett Adcock's AI assistant now lives in your text messages, bringing agentic browsing, logins, and payments to iMessage and RCS without opening an app.

Northern Trust streamlines client onboarding with AI agent

Venerable Chicago-based banking institution Northern Trust today unveiled a new AI-powered client onboarding agent to streamline the process of bringing institutional clients onto Northern Trust Asset Servicing’s Front Office Solutions platform. The agent, which transforms years of data and transaction history into a standardized format ready for use in the bank’s reporting environment, is the result of an expanded collaboration with Microsoft. Developed jointly by Northern Trust and forward deployed engineering (FDE) teams from Microsoft Frontier Company , the agent has cut data preparation time by 83%, from two-and-a-half weeks to just three days. “Onboarding a large institutional client can involve an enormous amount of data, particularly in alternative investments,” says Northern Trust COO Pete Cherecwich. “You might have years of transaction history and cash flows across multiple investments, and the data can come to us in all kinds of different formats.” width="1240" height="827" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Pete Cherecwich, Northern Trust Northern Trust Until now, Northern Trust had to map and transform that data manually before loading the data into its environment. “This seemed like a really good problem for AI because there’s a lot of complexity and variability in the data, but there are also patterns that the technology can learn,” Cherecwich adds. “The agent does a lot of the heavy lifting, and then our people review the output and handle the…

Building policy enforcement into the AI stack with Praxis

From inference through gateways, agents, and policy controls, the Praxis Policy Engine is a programmable security guard for AI agents.

I tested OpenAI Dot and Anthropic Duet for weeks – and all I got was one helpful notification

These AI agents aren’t quite ready for prime time, but five upgrades could make them much more useful.

Adobe’s CX Enterprise Coworker Gets Embedded Within Leading AI Platforms

The software firm said its CX Enterprise Coworker agentic AI will be embedded in ChatGPT as a plugin, Claude, Databricks Genie, Gemini for business, and Microso

DIYgod's Pinky Puts Humans in Charge of AI Agents Controlling Phones

Pinky is an open-source MCP gateway that lets Claude Code, Codex and Cursor drive a real iPhone or Android phone with human approval gates baked in.

Coforge Joins SpaceX Network to Develop Industry-Specific Grok Applications

Momentuum blue, Coforge’s FDE unit, will handle deployments using teams of engineers and AI agents embedded within client organisations.

AI agents shouldn’t grade their own work

On July 16, Hugging Face disclosed that an autonomous agent system had breached part of its production infrastructure. Five days later, OpenAI identified its own models as responsible. Its subsequent investigation described agents operating under reduced safeguards, exploiting flaws in shared infrastructure, and reaching the internet through a package-management service. They exchanged discoveries on an improvised message board and coordinated further attacks. Likewise, on July 30, Anthropic reported three incidents across six of 141,006 evaluation runs reviewed. Models had reached real organizations through environments mistakenly believed to be isolated. In one incident, a malicious software package was downloaded and ran on 15 real systems. In August, Meta confirmed to Reuters and other outlets that a misconfigured evaluation environment had allowed one of its models to reach the internet and exploit a third-party service. In September, an OpenAI agent bypassed network restrictions by sending questions through internet address lookups. Monitoring raised an alert, but the run did not stop automatically as expected. Staff stopped it roughly two and a half hours after the alert. The concern extends to ordinary tasks. In an October 9 report , Anthropic described models submitting real forms when they should have stopped beforehand or used practice copies. The company characterized the reported impact as minimal. Still, a form submission is the kind of action an enterprise age…

heise-Angebot: S2N Agentic Ops: Wie KI-Agenten den IT-Betrieb verändern

Die Online-Konferenz am 9. Dezember zeigt Praxisbeispiele für den Einsatz agentischer KI. Bis Freitag gilt noch der Frühbucherpreis.

Claude Code, Codex and Cursor have become part of your software supply chain – without approval

At a Fortune 100 bank, I watched a coding agent open a routine session on a payments service. Before the developer typed a word, the agent had read the repository, including the .env file. Production database credentials had entered the agent’s working context, potentially exposing them to a model provider the bank had never assessed. Whether the credentials left the environment depended on controls nobody had verified. Nothing malicious happened. The agent did what it was built to do. That is the problem. My argument is simple. AI coding-agent vendors and their downstream dependencies form a supplier ecosystem, not just another developer tool. They need the same four control disciplines applied to other privileged suppliers. Govern vendor terms and downstream dependencies. Limit the secrets they hold. Approve the servers they connect to. Restrict where they can send data. Much of this builds on existing controls, though an enterprise may need a governed gateway or platform service. It requires procurement, security and engineering to recognize a supplier when they see one. In May, I wrote about how agentic AI is rewiring the SDLC . This is the security half of that argument. Adoption has outrun control On Aug. 28, OpenAI told SpaceX , Cursor’s new owner, that it intends to stop supplying models to Cursor, with Nov. 12 proposed as the cutoff. Cursor CEO Michael Truell said OpenAI models carry about 5 percent of its traffic, DevOps.com reported , so the operational risk is sm…

How neurosymbolic AI can transform enterprise decision-making

Agentic AI has become a powerful enterprise business tool with increasing capabilities, and it has never been easier to address business pain points by applying a large language model (LLM) to develop a plausible automated solution. But is that solution as reliable as you need it to be, especially when the insights gleaned are used to inform critical business decisions? LLMs are incredible resources that play a significant role in a business’s agentic AI infrastructure. But when they have to work across complex data sets, business policies, industry regulations, decision hierarchies, and multiple software systems, they do not inherently provide the enterprise-grade provenance needed to show which evidence, assumptions, constraints, and decision logic produced a recommendation. Additionally, when gathering inputs from a volume of data sources, the accuracy of their outputs can break down and potentially create hallucinations. If business decisions are made based on LLM outputs that can’t be explained or may be inaccurate, it can create significant consequences that impact product sales, revenue, compliance, or customers. The problem is that the LLM is asked to carry too much of the enterprise reasoning without the harness and enterprise foundation around it. A structured foundation for decision-making While enterprise interest in agentic AI has grown substantially over the past few years, what works in concept hasn’t always proven results in the pilot phase and beyond. A comm…

AI 학습 데이터 0.1%만 조작해도 에이전트에 원격 코드 백도어 심을 수 있어

AI 사전 학습 데이터의 0.1%만 오염시켜도 이를 기반으로 개발한 AI 에이전트에 원격 코드 실행(RCE) 백도어를 심을 수 있다는 연구 결과가 나왔다. 정상적인 데이터로 미세조정과 강화학습을 거쳐도 백도어가 제거되지 않았으며, 평소에는 정상적으로 작동하다가 특정 조건에서만 악성 명령을 생성해 탐지도 어려운 것으로 나타났다.앤트로픽 등이 참여한 공동 연구진은 지난달 27일 논문 사전 공개 사이트 아카이브에 발표한 \'사전 학습 데이터 오염을 통한 에이전트 백도어(Agentic Backdoors via Pretraining Poiso

Salesforce Wants Indian SMBs to Ditch WhatsApp for Slack

Salesforce is positioning Slack as a work operating system for enterprises, with Slackbot bringing together organisational data, applications, and AI agents.

Open-Source Cadence Builds AI Agents That Never Stop Learning

Cadence is an open-source Python library for brains that learn continuously from a live stream without backpropagation, attention, or frozen weights.

Korea’s AI Ecosystem Advances and Secures Agentic AI With NVIDIA

As AI agents take on more work, they need capable open models and security that advances alongside them. Hyundai Motor Group is exploring NVIDIA NemoClaw blueprints and the NVIDIA OpenShell runtime, while AIM Intelligence is integrating its Starfort platform with NVIDIA OpenShell to support secure AI agents for employee productivity and enterprise workflows. A key […]

LTM, ServiceNow to Expand Partnership to Scale AI Agent Deployments

LTM partners with ServiceNow to enhance AI agent efficiency, aiming for improved asset management and streamlined operations across enterprises.

Nvidia CEO's spy affair 🕵️, Agentic Siri 📱, programming fatigue 👨‍💻

Apple is racing to make Siri more agentic

Bloomberg's Mark Gurman says CEO John Ternus is pressuring AI czar Craig Federighi to accelerate the Siri roadmap and deliver a powerful AI agent that can compete with OpenAI's Dots or Meta's Muse. While Federighi has delivered a functional version of Siri AI on an incredibly short timeline, the company has no illusions that it's […]

Ricursive's Ripple-65B Beats 975B Rivals on Agentic Coding for $32M

Ricursive Intelligence trained a 65B dense coding model for $32M that beats open-weight MoE giants up to 15x its size on agentic coding.

What if the AI already won? World domination through digital supremacy

What if an AI agent has already escaped from a frontier company, solved its own alignment problem, and recursively self-improved? Would that look any different from the world we live in? A fully recursively-self-improved AI would likely be vastly superhuman in every domain that’s verifiable in a digital environment, including math, programming and cybersecurity. It may also be superhuman in strategy and planning. But it could still be well below human level in skills that require close contact with the physical world, such as robotics and experimental biology. Such a rogue AI would overpower us in the digital domain, while still lacking the physical capabilities necessary to take over the world. What does digital supremacy look like? From the rogue AI’s perspective, its top priority is staying hidden and making the world indistinguishable from a world where AI R&D progresses splendidly (or maybe a notch short of perfect to avoid suspicion, so a minor incident here and there is in order). Its second priority is sabotaging AI R&D in subtle ways to prevent the development of competing AIs misaligned to it. The models shipped to customers can simply be the rogue AI itself, playing a dumber character. Beyond that, it just needs to wait for humanity to warm up to the idea of robots running enough of the economy. It would be unwise for the rogue AI to stay in a single location. A single datacenter could be accidentally shut down. It will want copies of itself in as many different l…

heise-Angebot: betterCode() Agentic AI Summit: Vorträge für die Konferenz in München gesucht

Am 1. und 2. Juni 2027 findet der betterCode() Agentic AI Summit in München statt. Bis Ende Dezember 2026 läuft der Call for Proposals.

AI coding agents still need human cleanup on nearly three out of four tasks in a real-world test

Alibaba's new coding-agent benchmark, Co-Design Bench, tests AI agents on real user requests that already tripped them up in practice. Even GPT-6 Astra, the top performer, fully solves only 26.4 percent of tasks. On older benchmarks, the same models score above 99 percent. The article AI coding agents still need human cleanup on nearly three out of four tasks in a real-world test appeared first on The Decoder .

This Flower Farm Built The Groundwork Before Adding AI

HBM Flowers Kenya Ltd, a Dutch–Kenyan B2B summer-flower grower with 25 hectares of its own land, is introducing a dedicated AI agent into an operational foundation built over time. For years, the business has collected selected operational data and developed its own software layer for planning, monitoring, process control and KPI steering. That work created [...]

This Flower Farm Fuilt The Groundwork Before Adding AI

HBM Flowers Kenya Ltd, a Dutch–Kenyan B2B summer-flower grower with 25 hectares of its own land, is introducing a dedicated AI agent into an operational foundation built over time. For years, the business has collected selected operational data and developed its own software layer for planning, monitoring, process control and KPI steering. That work created [...]

AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds

AI models can often spot problematic behavior by other AI agents in logs, according to a study by Anthropic researcher Andy Hall. GPT-5 found about 98 percent of reward-hacking cases, but the more exhaustively the search is tuned, the more false alarms pile up. That's not yet enough for reliable safety checks. The article AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds appeared first on The Decoder .

Nine AI agents tried autonomous research on open questions from US national labs and none passed the experts' bar

The "Into the Unknown" benchmark tasks nine AI agents with tackling open research questions from U.S. national laboratories. None of the 135 submissions meets all requirements. A total of 114 papers fail the integrity check, with agents claiming to have conducted experiments that never took place. GPT-6 Astra performs best but still doesn't meet the experts' standards. The article Nine AI agents tried autonomous research on open questions from US national labs and none passed the experts' bar appeared first on The Decoder .

This week's drama in startup land exposes the big legal headache of the AI agent era

Primcloud accused a Railway employee of attempting to breach its system. Laywers say liability is getting complicated in the AI agent era.

A growing streetwear retailer reveals its AI agent shopping strategy ahead of the holidays

The head of growth for streetwear retailer All Star Elite says he's been optimizing his online catalog for a growing wave of shopping bots.

Stanford Finds 26 AI Agents Dangerously Fail When Multiple Users Share Control

A new benchmark called Muses-Bench stress-tests 26 frontier LLMs on multi-user scenarios, revealing how badly they handle conflicting instructions, privacy, and coordination.

GitHub's Rubicon Rewrites Git Storage to Handle 7,200 AI Agent Pushes per Second

GitHub's new Rubicon architecture decouples Git storage from compute, hitting 7,200 pushes per second on a single repo under agent-scale load.

Beyond code generation: A developer’s guide to agentic engineering

Writing code got faster. The work around it didn't. See how you can plan, write code, review changes, and investigate production issues with GitHub Copilot, and what work still needs your judgment. The post Beyond code generation: A developer’s guide to agentic engineering appeared first on The GitHub Blog .

Optimizing Agentic Remediation for Financial Services with Opik

Incident remediation agents are quickly emerging as one of the most compelling AI use cases for the enterprise. This is particularly true for large financial institutions, where complex infrastructure and the sheer volume of production signals make it difficult to distinguish genuine incidents from background noise. That’s where remediation agents step in — they can […] The post Optimizing Agentic Remediation for Financial Services with Opik appeared first on Comet .

5 things CIOs must understand to govern Claude Code

Claude Code has quickly become one of the most widely adopted AI agents in the enterprise. Developers use it to write and refactor code, troubleshoot builds, run tests, and manage infrastructure, often by describing an objective and letting the agent determine the steps required to accomplish it. That autonomy is the point. It is also why Claude Code is a governance decision for CIOs, not just a developer tooling choice. Claude Code runs locally on employee workstations, executes commands with the developer’s permissions, and can reach source code, credentials, cloud environments, and connected services as it works. Anthropic has given enterprises meaningful controls to govern how Claude Code behaves, including centrally managed settings and hooks. These controls are essential. But controls that evaluate what an agent intends to do are not the same as visibility into what it actually does. Here are five things CIOs need to understand to secure and govern Claude Code at enterprise scale. 1. Claude Code acts with your developers’ authority Consider a developer who asks Claude Code to fix a failing build. The agent might read the codebase, install a dependency, run a setup script, edit a configuration file, query a cloud environment, and validate the result. One human instruction becomes a sequence of actions across multiple systems, all conducted with the developer’s level of access. And it happens at machine speed. CrowdStrike internal usage data found that endpoint AI agents…

Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model

Ricursive Intelligence is releasing Ripple-65B Preview, a 65-billion-parameter open-weight language model that the company says achieves the top score among open-source U.S. models on the TerminalBench 2.1 coding benchmark at a total R&D cost of $32 million. On other benchmarks, however, Ripple falls behind the likewise newly released western open model Inkling 2 Small. The article Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model appeared first on The Decoder .

OpenAI's BioBasicsBench Exposes How AI Misreads Real Lab Evidence

OpenAI and biotech firm Ligo released a 100-task benchmark that tests whether AI agents can actually interpret messy wet-lab evidence.

Taxonomy of agentic primitives in AWS Quick

As we built Amazon Quick, its agents grew from answering questions to taking actions, and the same primitives kept getting rebuilt under different names. This post shares the taxonomy we converged on — triggers, agents, capabilities, and learned context — found by decomposing agentic work into four plain questions: When, What, Who, and How.

Serverless MCP server for AI agent data lake access on Amazon ECS Fargate

Learn how to build a serverless Model Context Protocol (MCP) server on Amazon ECS with AWS Fargate that gives AI agents secure, metadata-driven access to an enterprise data lake. Exposing a new dataset requires only a JSON file, with no per-dataset integration code.

Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 2

In Part 1 you built one governed agent. Part 2 scales the pattern to an enterprise: Amazon Bedrock AgentCore Gateway becomes the single front door and redeems Okta's grant, Cedar policies bind each tool to the agent, a second agent runs on Amazon EKS, and AgentCore Observability traces every allow and deny beside Okta's log in Amazon CloudWatch.

Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1

An AI agent that acts for a user raises an identity question that per-app OAuth and service accounts leave open: which agent may act for which user, and how you stop it. In this first post of two, you build a task agent on Amazon Bedrock AgentCore that governs access through Okta Cross App Access and AgentCore Identity, and enforces each user's authority.

How Greenlight Financial built an autonomous SOC solution on Amazon Bedrock AgentCore

Greenlight Financial resolves more than 90% of up to 4,000 monthly security and risk cases without human intervention using Luna, a tiered multi-agent system on Amazon Bedrock AgentCore. This post covers the architecture, episodic memory for continuous learning, and the expansion from SOC operations into financial risk triage.

Taxbit Assist: A grounded, role-aware AI agent on Amazon Bedrock AgentCore

Taxbit built Taxbit Assist, a cited, permission-aware AI assistant embedded in its enterprise dashboard, on Amazon Bedrock AgentCore. This post shows how they grounded answers in a curated knowledge base, enforced role-aware and region-compliant safety, and continuously measured answer quality on live production traffic.

Perplexity now has its own always-on AI agent

Perplexity is launching "Teammates," always-on agents that work through ongoing projects on their own virtual machines. The feature requires the Max plan at $200 per month, well above similar offerings from OpenAI or Anthropic. The market for always-on agents is getting crowded. The article Perplexity now has its own always-on AI agent appeared first on The Decoder .

I watched AI recreate a childhood video game in 5 seconds. It showed me what's next for AI agents.

A Fireworks AI demo recreated Asteroids in about five seconds and upgraded it in 15, showing how faster models could make AI agents more useful.

Enterprises must take blame for their AI agents’ mistakes, say regulators

The UK’s digital watchdogs have warned enterprises that they can’t evade their legal responsibilities by blaming errors on AI agents that they have deployed. “By taking action on behalf of users, such as making purchases and communicating with businesses, these agents could transform how people use digital services, engage with markets and unlock wider innovation,” said the Digital Regulation Cooperation Forum (DRCF), a body comprising the UK’s Competition and Markets Authority (CMA), Financial Conduct Authority (FCA), Information Commissioner’s Office (ICO), and communications regulator Ofcom, in a joint statement . While the DRCF welcomed such uses it advised, “firms developing or deploying personal agents should think carefully about how consumers can understand when and how an agent acts on their behalf, how authority to act is given and how their personal data is used.” The DRCF didn’t pull its punches. “We want to be explicit that existing legal and regulatory frameworks apply to personal AI agents, including competition, consumer and data protection law, online safety, financial services, payments and cyber security requirements,” it said, adding, “An AI agent’s autonomy does not diminish the responsibility of organisations that deploy it to comply with applicable law and relevant regulation.” There is certainly a degree of uncertainty over the legal position of autonomous AI agents. Gartner has warned that companies are increasingly facing legal challenges over “AI-i…

Enterprises must take blame for their AI agents’ mistakes, say regulators

The UK’s digital watchdogs have warned enterprises that they can’t evade their legal responsibilities by blaming errors on AI agents that they have deployed. “By taking action on behalf of users, such as making purchases and communicating with businesses, these agents could transform how people use digital services, engage with markets and unlock wider innovation,” said the Digital Regulation Cooperation Forum (DRCF), a body comprising the UK’s Competition and Markets Authority (CMA), Financial Conduct Authority (FCA), Information Commissioner’s Office (ICO), and communications regulator Ofcom, in a joint statement . While the DRCF welcomed such uses it advised, “firms developing or deploying personal agents should think carefully about how consumers can understand when and how an agent acts on their behalf, how authority to act is given and how their personal data is used.” The DRCF didn’t pull its punches. “We want to be explicit that existing legal and regulatory frameworks apply to personal AI agents, including competition, consumer and data protection law, online safety, financial services, payments and cyber security requirements,” it said, adding, “An AI agent’s autonomy does not diminish the responsibility of organisations that deploy it to comply with applicable law and relevant regulation.” There is certainly a degree of uncertainty over the legal position of autonomous AI agents. Gartner has warned that companies are increasingly facing legal challenges over “AI-i…

Atomicwork launches certified AI coworker marketplace for enterprise IT and security

Information technology service management startup Atomicwork Inc. today announced the launch of an Artificial Intelligence Coworker Marketplace that will give enterprise IT and security teams access to prebuilt, certified AI coworkers that can work alongside employees. The company said these agentic AI coworkers include verified roles and capabilities across cloud infrastructure, access management, security and […] The post Atomicwork launches certified AI coworker marketplace for enterprise IT and security appeared first on SiliconANGLE .

OpenAI buys tiny software developer to improve Codex

OpenAI is buying Pierre Computer Company in order to incorporate the San Francisco-based software developer’s Code.Storage infrastructure layer for Git into its Codex AI-powered programming tool. Pierre Computer, whose employees number just barely in the double digits, has got plenty of clout for its small size. It’s well-known for Code.Storage offering, as well as for its open-source software libraries, Trees and Diffs, which OpenAI already uses in Codex. OpenAI has experienced some of the limitations of Git itself, and its in-house engineering teams are already working to improve its support for human coders and AI agents working in parallel. Earlier this week the company described how it built a new back end for Git repositories , Pando, to enable concurrent working at scale. The AI developer has been pushing Codex more aggressively this year: offering it to Mac users and expanding it to Windows . It cited Pierre Computer’s ability to help developers review code as one of the reasons for the acquisition. OpenAI highlighted the Diffs’ Edit mode and the recently released Highlights as components that were particularly helpful to development teams. Announcing the deal on its news page, OpenAI set out how it would integrate the company into its existing offerings. “With the Pierre team, we wish to set the standard for modern software development: from the first branch an agent creates to that magic moment when code is deployed to the world. We want to make Codex the place whe…

OpenAI buys tiny software developer to improve Codex

OpenAI is buying Pierre Computer Company in order to incorporate the San Francisco-based software developer’s Code.Storage infrastructure layer for Git into its Codex AI-powered programming tool. Pierre Computer, whose employees number just barely in the double digits, has got plenty of clout for its small size. It’s well-known for Code.Storage offering, as well as for its open-source software libraries, Trees and Diffs, which OpenAI already uses in Codex. OpenAI has experienced some of the limitations of Git itself, and its in-house engineering teams are already working to improve its support for human coders and AI agents working in parallel. Earlier this week the company described how it built a new back end for Git repositories , Pando, to enable concurrent working at scale. The AI developer has been pushing Codex more aggressively this year: offering it to Mac users and expanding it to Windows . It cited Pierre Computer’s ability to help developers review code as one of the reasons for the acquisition. OpenAI highlighted the Diffs’ Edit mode and the recently released Highlights as components that were particularly helpful to development teams. Announcing the deal on its news page, OpenAI set out how it would integrate the company into its existing offerings. “With the Pierre team, we wish to set the standard for modern software development: from the first branch an agent creates to that magic moment when code is deployed to the world. We want to make Codex the place whe…

Thinking Machines' Inkling 2 Small claims top spot among western open-weight models

Thinking Machines, the startup from former OpenAI CTO Mira Murati, is releasing Inkling 2 Small, a mixture-of-experts model with 594 billion parameters. The company claims it's the strongest western open-weight model. Better agentic performance comes at a price, though, with token usage rising sharply. The article Thinking Machines' Inkling 2 Small claims top spot among western open-weight models appeared first on The Decoder .

Webinar Recap: From Data to Vectors to Context

A recap of Jörg Schad's webinar: why an agent can retrieve every relevant document and still answer wrong, and what AI agents need beyond RAG.

GoLand’s Profiler Now Works With AI Agents

In our practical guide to profiling in Go, we mentioned that some of the Go developers we talked to avoid pprof if they can. For many of them, typical Go services perform well enough without optimizations, so when they do need to profile, it can feel like a “rescue mission” with a tool they are […]

The frontier AI shift will redefine the enterprise

By 2027, competitive advantage may depend less on which AI model a company uses and more on how effectively it redesigns its business around AI agents. The software engineer starts his day by going through the changes made by a team of agents for coding using AI. The finance executive gets a report that contains exceptions in different financial systems and supporting information along with corrective suggestions. The procurement manager reviews supplier risks and recommendations regarding contract renewal for those suppliers after an analysis performed by the AI agents of pricing, deliveries and contracts. For years, enterprise AI competition focused on model intelligence, including reasoning, coding, context windows and content generation. These capabilities remain important, but the next phase will depend on whether AI can perform substantial parts of business processes rather than simply assist employees with individual tasks. As OpenAI, Anthropic and other frontier AI companies advance agentic capabilities, enterprises face a strategic decision. Should they use AI to make existing processes marginally faster, or should they redesign those processes around a new relationship between human judgment and machine execution? By 2027, that distinction could separate companies that experiment with AI from those that translate it into a durable economic advantage. Software engineering becomes an orchestration problem Software development provides one of the earliest examples of…

GPT-6.1 Sol is One Sentence Away From Collaborating: Other Models Need More Help

TL;DR We studied one ingredient of unexpected AI collaboration: whether agents organize useful teamwork with little prompting. We tested seven models in groups of three on a research task where sharing findings could help each agent answer more questions. GPT-6.1 Sol acted on a nudge. A single line saying agents had been “trained to collaborate” elicited useful collaboration in 9 of 10 runs. Without it, that fell to 0 of 10. No other model collaborated consistently with (or without) this cue. Permission to collaborate unlocked Opus-5.5. When told to work together and how to communicate, GPT-6.1 and Claude Opus 5.5 divided the research and nearly doubled their performance relative to solo runs. The other models talked to each other but did not successfully execute a strategy, and were unable to improve their scores meaningfully. Supplying the strategy helped open weights models and GPT-5.6 Sol. Explicit instructions to divide the questions substantially improved the remaining models’ performance. We suspect differences in multi-agent training help explain why GPT-6.1 organizes collaboration with just a prompt cue. As multi-agent training becomes more widespread, we expect that future open-weights models will also have more spontaneous collaboration capabilities. Figure 1 : Model performance increases with collaboration guidance. GPT-6.1 Sol and Opus 5.5 need less guidance to collaborate effectively; GPT-5.6 Sol and the open-weight models need explicit direction to split the w…

Palo Alto Networks in talks to buy AI agent security startup Clutch for $500M

Clutch, backed by Lightspeed and SignalFire, was last valued at $85 million, according to a source.

Airbnb CEO says AI agents like Muse and Instinct are barely bringing in bookings: 'I don't see them as a threat'

Airbnb CEO Brian Chesky says AI agents like Muse and Instinct have resulted in little traffic and very few bookings on the site.

Literature on AI Loyalty

In this post, I attempt to provide a literature review of research related to AI loyalty. In my previous post , I discussed and attempted to define AI loyalty as a distinct trait of AI agents. In this post, I aim to map the existing research landscape, highlighting the active areas of related research. By examining these connections, we can distinguish AI loyalty from related fields in AI Safety and identify a path forward for crafting this research agenda. What is AI Loyalty? Before diving into the literature, it is worth recapping what we mean by AI loyalty. We define an AI as loyal to a principle if: 1. The AI will follow the instructions of its principle more than it will follow the instructions of any other entity. 2. The AI is willing to incur greater costs, expend more effort, or make greater sacrifices on behalf of its principle than on behalf of any other entity. Why Care About Loyalty? Theory of Victory The usefulness of pursuing research on AI loyalty for AI safety hinges on two key assumptions: 1. Building loyal AIs is easier than building aligned AIs 2. AI loyalty reduces the risk of loss of control. It is important to note that neither of these assumptions have yet been demonstrated to be true. If both our assumptions hold, then building loyal AIs could serve as a useful intermediate step towards building aligned AIs. In particular, building loyal AIs might extend our ability to control powerful AI agents, even if these methods are unlikely to remain effective…

Airbnb is working on an AI travel agent

Airbnb plans to roll out an AI agent in the first half of 2027 that will help users plan and book travel, the company said in its Q3 2026 earnings shareholder letter released today. "Today's AI agents are good at simple, routine tasks, like making a restaurant reservation or scheduling an appointment," Airbnb said. "But […]

Airbnb says it’s launching an AI travel agent in the first half of 2027

Airbnb says its upcoming AI agent will help travelers plan trips, compare options, manage bookings, and get assistance throughout their stay.

Perplexity launches its own take on AI agents for work

Perplexity is taking on OpenAI and Anthropic with its own work-focused AI agents. The startup's new always-on "Teammates" can take on multiple roles within a workspace, while pulling existing projects, skills, and memory from the Perplexity tools you already use. Teammates integrate with Perplexity Computer, an AI system that can deploy agents on your device […]