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q3ok / coordinated-agent-team

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Coordinated Agent Team is a prompt-driven multi-agent system for autonomous software delivery. It defines clear agent roles, a deterministic workflow, strict I/O contracts, and artifact-based coordination.

agent-orchestration ai-agents autonomus-systems developer-tools multi-agent
30
6
100% credibility
Found Feb 18, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
JavaScript
AI Summary

A framework with instructions for coordinating a team of specialized AI agents to autonomously plan, build, test, and document simple web applications through chat in a code editor.

How It Works

1
🔍 Discover AI Team Builders

You stumble upon a clever system where a team of smart digital helpers works together to create apps for you, just by chatting with them.

2
📂 Prepare Your Project

You add a set of helpful instructions to a special folder in your own project, getting ready for the team to join in.

3
💻 Open Your Workspace

You open your project in your favorite coding program that has a built-in chat feature for talking to helpers.

4
🗣️ Tell the Team Leader Your Idea

In the chat window, you describe exactly what kind of app you want, like a greeting card maker or a timer, and the leader takes charge.

5
👥 Watch the Team Collaborate

The leader directs specialists to plan, design, code, review, test, and secure your app, updating progress files you can peek at.

6
❓ Help When Asked

If the team needs your input on a decision, they pause and ask you directly in the chat, and you reply simply.

🎉 Enjoy Your New App

Your fully working app is complete with a detailed report explaining everything, ready to use or share right away.

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Star Growth

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AI-Generated Review

What is coordinated-agent-team?

Coordinated-agent-team is a JavaScript-based multi-agent framework that automates software delivery by assigning clear roles to AI agents—like spec writers, architects, coders, reviewers, QA testers, and security checkers—coordinated by a central orchestrator. Drop the agent prompts into your project's .github/agents folder, then kick off tasks in VS Code's GitHub Copilot Chat with "@orchestrator build X". It solves quality drift in single-agent AI coding by enforcing deterministic workflows, artifact-driven quality gates, and repair loops for autonomous fixes.

Why is it gaining traction?

It stands out with role separation for specialized agents using top LLMs (Claude, GPT, Gemini), lean vs. full workflows for trivial vs. complex tasks, and strict gates that block progression on failures like bad reviews or security issues. Developers hook on the end-to-end ownership—from spec to release—producing working JS web apps (greeting generators, Pomodoro timers, traffic sims) without manual orchestration. Coordinated AI agents deliver consistent results where solo agents falter.

Who should use this?

Solo JS devs or small teams building vanilla web apps, like frontend prototypes or utilities (timers, simulators), who want AI to handle full cycles beyond quick code gen. Ideal for experimenters testing coordinated multi-agent reinforcement learning in dev workflows, or those advancing artifact-driven autonomous delivery without infra overhead.

Verdict

With 14 stars and 1.0% credibility score, it's early-stage—docs are solid but lacks broad tests or adoption; treat as a promising experiment for Copilot power users. Try on toy projects if you're into coordinated AI agents, but pair with human oversight until maturity grows.

(198 words)

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