Posts tagged Orchestration — AI × software engineering, made practical.
You will learn how to treat the human in a multi-agent pipeline as a slow, rate-limited agent and build the protocol around those limits with escalation budgets, async queues, decision packets, timeout defaults and two metrics that show whether the setup is working.
You'll learn how to structure a multi-agent system as a deterministic pipeline with a single bounded swarm, which can only hand off its output through an exit contract that a code checkpoint enforces.
You'll learn how to partition parallel agent work and design the merge step (output contracts, shared-file ownership, seam reviewers and reducer budgets) so that fanning out actually saves time, and when you shouldn't fan out at all.
A promotion backed by every number on the page quietly slowed my team down — and the blind spot behind it turns out to be the same one that makes most multi-agent observability stacks credit the wrong agent.
One 429 turned into a fan-out storm in under ten minutes — here's the assumption baked into every rate limiter that agentic callers blow straight through, and the three fixes that actually hold.
A billing agent and a policy agent both answered correctly, and the orchestrator still shipped a wrong response to the customer — because nobody had built the one piece that decides what happens when confident agents contradict each other.
If your first multi-agent run came back with confident garbage, the fix isn't a better model — it's the same delegation skill you already learned the hard way with your first junior hire.