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Why this matters
Most teams discover multi-agent failure modes the same way: a critical demo, a senior stakeholder, a confidently-wrong agent. The patterns below come from shipping these systems into customer-facing flows where “model picked the wrong tool” is a P1.
The hard part of multi-agent isn’t the agents — it’s the protocol they use to talk to each other.
Three patterns that pay off
- Strict tool schemas with provenance. Every tool call records which agent invoked it, with what arguments, and what came back. Replay becomes trivial; debugging stops being archeology.
- Bounded autonomy ladders. Each agent has a maximum number of self-directed steps before control returns to a supervisor. This caps blast radius without sacrificing the value of agentic loops.
- Eval-as-deployment-gate. A regression suite of real customer prompts, run on every model swap. If win-rate drops, the deploy doesn’t ship.
Building one of these systems?
I help senior teams ship production agentic systems. Happy to compare notes.
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More on each of these in follow-up posts. If you’ve hit a failure mode that doesn’t fit one of the three above, I want to hear about it.