Multi-Agent Orchestration Patterns That Survive Production

The model is no longer your differentiator — everyone rents the same frontier models. What separates the agentic systems that ship from the ones that demo is the orchestration layer: who plans, who executes, what happens when step 7 of 12 fails at 2AM.

One supervisor agent owns the plan and the user-facing thread; specialist agents own narrow jobs with narrow tools — retrieval, analysis, drafting, compliance checks. The supervisor decomposes, routes, integrates, and decides when the task is done. Why it survives production: it gives you a single audit spine (every decision flows through one place), specialists stay simple enough to test, and you can swap a weak specialist without disturbing the system. In LangGraph terms: supervisor node, typed state, specialist subgraphs, explicit edges. Resist the "swarm" of free-talking peer agents — emergent behaviour is precisely what an auditor can't sign off.

When subtasks are independent — screen 40 counterparties, summarise 12 filings — fan out in parallel and join. The discipline part: cap concurrency (your rate limits are a shared resource), set per-branch timeouts, and design the join for partial results. A join that requires all branches to succeed converts one flaky branch into a system-wide failure. 38 of 40 with two flagged "unverified" beats a perfect answer that never arrives.

Demos plan for success; production is mostly handling non-success. The machinery that matters: checkpointed state so a crash resumes instead of restarts; bounded retries with circuit breakers per tool; termination conditions on every loop — max steps, max tokens, max wall-clock; and an escalation path that packages context for a human instead of dying silently. Every runaway-agent incident I've debugged traces back to a missing termination condition. Every one.

Bottom line: buy models, build orchestration. The graph, the state, the recovery paths — that's the asset, and it's the part your competitors can't rent.