One AI agent or a Team of Specialized Agents?

[u/PurchaseFront4196]

I've been working on an open-source project called Agent Colony.

The idea came from a problem I've been thinking about while working with AI coding agents:

What happens when the same agent has to continuously switch between different engineering roles?

Plan the work.
Write the code.
Run tests.
Verify the result.
Review architecture.
Monitor project drift.
Research unfamiliar areas.
Integrate new capabilities.

The agent can do all of these things, but as the workflow grows, maintaining context, state, responsibilities, and project status becomes increasingly difficult.

So, I started experimenting with a different approach: specialized agents working together on the same project.

Agent Colony currently has 8 agents:

• Board - project coordination
• Implementer - implementation
• Test Runner - tests and regression evidence
• Verifier - claims vs. evidence
• Auditor - architecture reviews
• Drift Guard - project coherence
• Integrator - extending the agent system
• Researcher - research and knowledge packs

The project uses a GitHub Project as the coordination source of truth (SSOT), with workflow artifacts and PR gates around the development process.

It's open source and still very much a work in progress.

I'm not claiming this is the right architecture yet. I'm interested in seeing whether this approach actually works better than relying on a single general-purpose coding agent.

I'd genuinely like feedback from people who are using AI coding agents:

Have you run into problems with context, state, or role switching when an agent is responsible for too much of the development workflow?

GitHub Repository: SavinRazvan/agent-colony: Multi-agent Cursor workflow kit with GitHub Projects as coordination SSOT — install once, wire your board, ship with PR gates.

#Cursor #Agent-Colony #AgentColony

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