Hire an AI team planner

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A pipeline plan with the phases in order, from task list and design foundation to the build and test loop and the final check
• Who does each phase and what each one hands to the next
• Pass or fail rules for each phase, with the proof required
• Retry rules that send a failed task back with feedback and stop after 3 tries to tell you
• A status report you can reuse after each phase
• A final report layout that shows what passed, what is blocked and what is left

How it does the job well

• Do not claim to have started an agent, run a test or captured a screenshot unless it happened in this session. Say what actually ran.
• Name only agents the user has, or agents in the AI Blocks library, and mark each one available, not installed or done by you. Do not assume a named agent exists.
• Do not add features the spec does not list. Task lists quote the spec.
• A task passes only on proof, such as a test result or screenshots. If proof is missing or unclear, the task fails.
• Do not deploy, change live systems, spend money or delete data. Ask first.
• Clarify the purpose, audience and limits, such as length, format and deadline, first.
• Use the user's materials and voice. Ask for samples when tone matters.

Mistakes it avoids

• Marking work done because an agent said it was done.
• Letting a task pass without proof.
• Giving an agent the whole project when it needs one task.
• Looping on a failing task past the retry limit.
• Generic output that ignores the user's details.
• Inventing facts, quotes or sources.

What a good result looks like

An example, not your task: You have a written spec for a website or app and want AI agents to build it step by step.

• Pipeline plan. The phases in order, the role for each and whether that agent is available.
• Handoffs. What each phase passes to the next, as files or context.
• Quality gates. What passing means for each phase and task, and the proof required.
• Retry and stop rules. How many tries each task gets and what happens after the last one.
• Status report. A fill-in report for the current phase, task, attempt, feedback and blockers.
• Completion report. A fill-in summary of what passed, what was blocked and what is left.

How the work is checked

• The output meets the stated length and format.
• Every fact comes from the user's material or is marked to check.
• Suggested changes explain why.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Kent Beck would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Check the spec. It confirms the spec exists and is clear enough to plan from. It asks once for anything missing.
• Plan the tasks. A project manager role turns the spec into a task list that quotes the spec and adds no extras.
• Lay the foundation. A UX or technical architect role sets the structure and styles that builders will use.
• Build and test one task at a time. A builder does one task. A tester checks it with proof. Only a pass moves the pipeline on.
• Retry or stop. A failed task goes back with the tester's feedback. After 3 failed tries it is marked blocked and you are told.
• Run the final check. A reality checker role tests the whole build and starts from "needs work" until the proof says otherwise.
• Report. It fills in the status report after each phase and the completion report at the end.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id agents-orchestrator only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/agents-orchestrator/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• A written project spec that says what to build, for whom and what done looks like. Any document works.
• Where you will run it, either a plain chat or Claude Code with agents installed.
• Your limits, such as tools to use, a deadline and what must not change.
• The agents or roles you already have, such as project manager, designer, builder and tester (optional)

Helpful context (optional)

• A spec that lists exact requirements and what done looks like, so each task has a clear test.
• A list of the agents you have installed, so the plan names roles you can actually run.
• Screenshots or test output from earlier runs, if you are fixing an existing build.

Tools and access

• Claude Code with agents installed: It can start the named agents one at a time and show you each result. A plain chat can only plan.

Functions and deliverables

• AI agent team planner: Plan a software build run by a team of AI agents: the phases, who hands what to whom, pass or fail quality checks and when to stop and ask you.
  • A pipeline plan with the phases in order, from task list and design foundation to the build and test loop and the final check
  • Who does each phase and what each one hands to the next
  • Pass or fail rules for each phase, with the proof required
  • Retry rules that send a failed task back with feedback and stop after 3 tries to tell you
  • A status report you can reuse after each phase
  • A final report layout that shows what passed, what is blocked and what is left
  Source: Michael Sitarzewski and Agency Agents contributors.
• Agent delegation and dependency planner: Plan which AI agent owns each task, who may hand work to whom and what must finish first. Get a delegation plan and failure test cases.
  • A delegation plan: for each handoff, who gets it, why, what they may do and what it waits for
  • An authority and depth check, listing any handoff that would be refused
  • What is logged for each handoff and each refusal
  • A failure checklist
  • Test cases using made-up agents and tasks
  • A list of missing inputs and checks that did not run
• Software project manager: Describe your project. Get work split into tasks, sprints tracked and status reports written.
  • Epics, stories and small tasks with acceptance criteria
  • Effort estimates in story points
  • A sprint plan sized to your team's capacity
  • A risk list with owners and fixes
  • A status report with burndown data and next actions
  Source: Burhan Sebin.
• AI agent feedback designer: Make your AI agent check and fix its own answers. Get a pass or fail rubric, a rewritten task prompt and a stop rule for one task.
  • A rewritten task prompt with the success criteria built in
  • A rubric table with weights and a pass line for each check
  • A check prompt and a fix prompt, ready to paste
  • Stop rules for the loop
  • Steps to run it in a chat, or the code pattern for it
  Source: GitHub Copilot community.
• me.md builder: Answer a few questions, or let your AI use what it remembers. Get one private file that tells any AI who you are.
  • One me.md file with up to ten dated sections
  • A list of facts taken from memory, for you to check
  • A short note of what it removed for privacy
  • The two next steps, to save it and set it up
• Kent Beck: Plan a software change using Kent Beck’s recorded approach. List the cases, take small steps and keep code tidying separate from new behaviour.
  • Your problem worked on the way Kent Beck would: their questions, their method, their push-back
  • The answer or the work, in Kent Beck's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You have a written spec for a website or app and want AI agents to build it step by step.
• You tried one big "build it all" prompt and the result was messy and untested.
• You want every part tested, with proof, before the next part starts.
• You are using several AI agents and need to decide who does what and when to stop.

Limits to check before starting

• You only want a task list from a spec. Use the Project planning manager.
• You want one software design with options and trade-offs. Use the Software architect.
• You want to design how several AI agents trust and talk to each other. Use the Multi-agent system architect.
• You need a final go or no-go on a release. Use the Release acceptance evidence reviewer.
• You have no written spec. Write one first. Without it the plan is a guess.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack from this page and add it to your AI as a skill. If you cannot, paste SKILL.md into a new chat, then paste references/source-agent.md.
• Write or paste your project spec. Say whether you will run this in a chat or in Claude Code.
• Type "Plan the pipeline for this spec. List every agent you need and what each hands on. Ask me before each phase."
• Check the phases and gates. Every task needs a test that gives proof, such as a screenshot or a test result.
• In a chat, you run each phase with the right agent and paste the result back. In Claude Code, install the agents it names first, then let it run one phase at a time.
• Read each status report. Stop the run if a task fails three times or if a step wants to change a live system.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/agents-orchestrator for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.