Multi-Agent Coding Systems: Orchestrating Specialist Agents for Complex Tasks
An architectural guide to multi-agent coding systems where specialist agents handle planning, implementation, testing, and review in coordinated pipelines.
One model is not enough for hard problems
Complex engineering tasks benefit from separation of concerns: a planner agent decomposes the goal, an implementer writes code, a tester generates and runs tests, and a reviewer checks the result. Each agent can use a different prompt, context window, or even model size.
Coordination is the hard part
Agents need a shared workspace, a clear handoff protocol, and a way to detect when a step has failed. Without explicit state management, agents can loop, contradict each other, or silently drop requirements.
Keep a human in the loop for now
Multi-agent systems are powerful but can produce convincing-looking work that is subtly wrong. A human checkpoint after planning and before merge catches issues that individual agents cannot self-diagnose.
Conclusion
Multi-agent coding pipelines can tackle larger tasks than any single agent, but they trade simplicity for capability. Start with two agents and expand only when the coordination overhead is justified.
Use AI to expand the amount of thinking your team can verify — never to remove verification from the loop.
