Principles Behind Multi-Agent Coding

Why Multi-Agent Coding Matters Now

A new class of developer workflows is emerging: not just one AI helping you code, but teams of agents planning, coding, and checking work alongside you. Multi-agent patterns promise faster turnaround and better coverage on complex tasks—but only if we use them in ways that are understandable, debuggable, and worth the extra coordination.

Three Ways People Use the Word "Multi-Agent"

1. True Multi-Agent Systems

Explicit, independent agents (planner, coder, tester, etc.) with their own prompts, tools, and state, coordinated by an orchestrator.

2. Simulated Multi-Agent Systems

One LLM session playing multiple roles ("planner," "coder," "reviewer") inside a single shared context, usually inside tools like Claude Code or Copilot.

3. Human-Owned Multi-Session Workflows

A human developer juggling several independent LLM sessions or branches in parallel (e.g., separate worktrees for feature work, tests, and refactors) and acting as the orchestrator.

Our Principles for Multi-Agent Workflows

1. Get clear on what you mean by "multi-agent."

Clear labels reduce confusion and make it easier to reason about behavior and tradeoffs. We define multi-agent workflows by our "true" definition above. In our opinion, multi-agent workflows are about managing context through independent conversations, artifacts (e.g., plans, specs, context), and control flow.

2. Be skeptical about multi-agent hype.

Multi-agent workflows are easy to romanticize and even easier to waste time on. They can encourage shallow thinking, constant context switching, and lots of orchestration work with little real gain. Treat multi-agent setups as experiments you have to justify, not as a default. If you notice your deep-focus work suffering or you're mostly "managing agents" instead of shipping changes, dial back to simpler single-agent flows.

3. Keep the human in the loop.

Developers should still set intent, review diffs or artifacts, and make final decisions. Multi-agent patterns should increase leverage, not remove judgment or blur accountability. Using spec-driven development or another workflow that forces you to get clear about your intent on the front-end is critical for success with a multi-agent system.

4. Parallelism should serve flow, not fight it.

We use parallel agents to reduce waiting and end-to-end latency, not to encourage frantic context switching. The goal is less "juggling many shallow tasks" and more "freeing time for deep work." As you begin working with multi-agent systems, think, "How can I do this single task better and faster?", not "How can I do many tasks at once?"

5. Design explicitly for coordination overhead.

Multi-agent systems add "cat-herding" risk: routing, handoffs, conflicts, and failure modes. Any multi-agent workflow we endorse needs clear control flow, shared artifacts, and good debuggability. Understand how you will identify errors and judge success.

6. Expect evolution.

Developers will continue to build better workflows, and those patterns often become the next generation of best practices. We take those emergent workflows seriously as signals for where to invest in productized, well-supported multi-agent architectures.

Conclusion

We're early in the history of multi-agent coding, and these patterns are just starting to emerge. In the near future, we'll go deeper on practical multi-agent coding patterns, with concrete workflows, examples, and guidance for teams ready to take the next step.

Ready to explore multi-agent workflows for your team? Schedule a conversation with DevClarity to discuss how these principles can apply to your specific development context.