Multi-Agent Orchestration: How to Build AI Systems That Actually Handoff Correctly

The Problem with Multi-Agent Systems Most multi-agent systems fail not because the individual agents are dumb—but because the handoffs between them are broken. One agent produces output, another expects different input, and suddenly you have a cascade of failures. After building and running 8+ production AI agents, I've learned that orchestration isn't about making agents smarter. It's about making handoffs explicit, verifiable, and recoverable. The Three Handoff Failure Modes Schema Mismatch — Agent A outputs JSON, Agent B expects a different shape Lost Context — Critical information gets dropped between agents Silent Failures — Agent B succeeds but produces wrong output because it misunderstood Agent A's intent A Practical Framework Here's the pattern I use for reliable handoffs: Key Principles Explicit contracts over implicit expectations. Every handoff has a typed contract. If Agent A says "success", Agent B knows exactly what that means. Verification before passing. Never pass out