I’m a UI/UX designer who uses Codex across product, design, research, data, content, and development work. As the projects became more complex, I found that the hard part of a multi-Agent workflow was not spawning more Agents.

The hard part was designing a persistent team where every Agent has a non-overlapping reason to exist, each role gets an appropriate model configuration, and Skills are matched to real work instead of added as decoration.

Build AI Team is not an app-development template. The framework is designed for software, product, design, research, data, content, migration, and release workflows.

1. Start with ownership, not job titles

Work backward from the user’s goal to a verifiable final state. List the indispensable work packages: professional judgment, production responsibility, independent verification, coordination, and any external action that needs separate permission.

Give every necessary work package one clear Owner. Then test every proposed Agent by transferring its responsibility to the closest existing Owner.

Keep a separate Agent only when merging would:

  • leave a deliverable without one Owner;
  • break permission isolation or independent review;
  • combine professional methods that cannot be handled reliably together;
  • remove parallel capacity required by the user’s actual deadline; or
  • make the remaining role misleading about its responsibility.

When coordination is needed, one existing substantive role can also serve as project lead. The lead manages the plan, cross-role dependencies, delivery status, and final handoff, but does not gain ownership of other roles’ professional work.

For maker–checker work, the independent reviewer stays outside production and does not edit the work being reviewed.

2. The same test works across different scenarios

  • A complete paid SaaS may need distinct product, UX/UI, engineering, and independent-quality ownership.
  • A research project may need separate evidence responsibilities because different sources have different permissions, while one Owner remains responsible for the final synthesis.
  • A CRM or data migration may need one execution Owner and one independent read-only verifier.
  • A content task, design exploration, or narrow prototype may stay with one Agent when the work is continuous and no ownership or review boundary would be lost.
  • A release workflow may separate preparation, technical execution, independent verification, and the final human authorization to publish.

The useful question is not “How many Agents should this type of project have?” It is “What responsibility, control, or collaboration value would be lost if this Agent disappeared?”

3. Route models by responsibility

Model choice is part of each role’s design, but it should never determine how many Agents exist.

For every retained role, I separate:

  1. Core recurring work — the work that repeatedly determines the role’s main result.
  2. Occasional high-risk work — architecture, conflicting evidence, security-sensitive judgment, or subtle review that appears only at certain stages.
  3. Low-risk, easy-to-check work — bounded formatting, extraction, transformation, or mechanical checks.

The default model and reasoning effort should follow the core recurring work. A local high-risk step can become an explicit upgrade condition instead of forcing the whole role onto the strongest configuration.

Token control comes from three places:

  • keeping one Agent when extra persistent roles add no concrete value;
  • assigning each role a model and reasoning effort suited to its actual work instead of giving the whole team the same strongest configuration;
  • using scripts or deterministic tools for fixed, mechanical, independently verifiable steps while the model remains responsible for interpretation, exceptions, and professional judgment.

Any model change should name the complete target model and reasoning effort available in the current environment. It should not promise a fixed saving percentage or reduce high-risk professional work merely to use fewer Tokens.

4. Match high-value Skills to owned work

A Skill is valuable when it materially improves a current responsibility, owned result, or immediate next step. It should not create a role, expand the user’s goal, or silently fill missing professional ownership.

I use this order:

  1. Check Skills the current host explicitly exposes as available.
  2. Use a small offline index for relevant public leads.
  3. Only when the user requests live discovery, search public sources and review the original repository.

A useful recommendation should identify the role it helps, the concrete result or risk it improves, its exact source, whether it is locally available or only a public candidate, and its important license, maintenance, dependency, script, network, login, and permission boundaries.

More recommendations are not better. The initial list is capped at three and omitted entirely when no Skill has clear value.

For example, an authentication or API security Skill may help an engineering or independent-review responsibility in a paid SaaS project. That does not automatically justify a separate Security Agent, and recommending the Skill does not mean it is installed, current, safe, or authorized to run.

5. Explicit invocation avoids unnecessary overhead

Build AI Team is explicit-invocation only. Ordinary Codex requests and casual mentions do not activate it.

This keeps routine work from being forced through a team-planning workflow and helps avoid unnecessary context, planning, and coordination overhead.

The runtime also stays deliberately small:

  • one English runtime semantic source, while clarification questions and recommendations follow the user’s request language;
  • five versioned runtime files that must stay together;
  • no background service, product account, or additional API key;
  • no generated AI_TEAM.md, roster, or duplicate team-state document to maintain;
  • persistent Codex tasks themselves remain the live team state.

These choices are intended to reduce rule drift and ongoing maintenance without claiming a fixed Token or cost reduction.

6. Every consequential action remains separate

The first response is a reviewable plan.

Planning, persistent-task creation, public Skill search, installation, script execution, login, private-data access, external writes, uploads, publication, and deletion are separate actions. Confirming one does not authorize the others.

The open-source Skill

I turned this framework into an open-source Codex Skill called Build AI Team.

It produces:

  • one Agent or the smallest reliable persistent team;
  • one Owner per work package and an explicit project lead when applicable;
  • role-specific model and reasoning configurations;
  • optional upgrade and Token-saving paths;
  • up to three high-value Skill matches with clear status and sources;
  • explicit collaboration, review, dependency, and permission boundaries.

Example:

$build-ai-team I need a reliable workflow for reviewing research, preparing a report, and publishing the final version.

Codex Desktop project-based usage is the primary tested target. The current validation is limited real-host evidence, not a guarantee across every host or scenario.

I’d be interested in two questions:

  1. Where has role-specific model routing improved quality or reduced unnecessary Token use in your multi-Agent work?
  2. What makes a Skill recommendation genuinely valuable rather than another item in a long tool list?

Disclosure: I created and maintain the project.

GitHub: search for itsedizeng/build-ai-team.