Engineers are shifting from single AI assistants to small agent teams with specific roles: planner, UI builder, backend coder, tester, documentor, and orchestrator. Free templates work in LangChain or CrewAI. The pattern – assign, validate, loop – matters more than the tech.
The story
Agentic AI coding teams are moving from “fun demo” to “normal workflow.” What I keep seeing is this: engineers stop asking one assistant to do everything and start running a small *team* of agents with narrow roles, plus a supervisor to keep everyone honest.
Why now? Two things are lining up. First, software work is already social: you plan, build, review, test, and document. Second, AI output gets better when you don’t treat it like a lone genius. It works best as a group with clear jobs and clear boundaries.
So I put together *free, customizable instructions* for an agentic coding team that you can copy into tools like LangChain, CrewAI, AutoGen, or LangGraph. No paid frameworks needed.
The structure is boring on purpose – boring scales:
– Planner: breaks a request into small tasks with dependencies, using structured output like JSON.
– UI agent: builds interface pieces with accessibility and responsive behavior in mind.
– Backend agent: writes core logic and APIs, with error handling and basic security habits.
– Tester: writes unit/integration tests, hunts edge cases, reports failures for replanning.
– Documentor: produces a README and API docs with examples.
– Orchestrator: routes work, checks adherence, resolves conflicts, and enforces “no deployments.”
The workflow pattern is the point: capture intent, assign work, run in parallel, validate in loops, then optional human approval. Shared memory helps too – like a vector store [FAISS works] – so agents don’t “forget” what the others decided.
If you’re leading a team, the question isn’t “agentic or not.” It’s: which tasks deserve autonomy, and where do you want a consent-based checkpoint before anything touches production?
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