@alex_prompter: Multi-agent AI setups break at four points. Routing misfires, parallelism never happens, handoffs lose context, and cov…

Multi-agent AI setups break at four points. Routing misfires, parallelism never happens, handoffs lose context, and coverage gaps hide. Fix all four and the system runs itself. 1. A dispatch matrix that routes by pattern, not inference Build a table mapping signal words to agents. Pricing, supplier, shipping → procurement agent. Email, customer, pipeline → sales agent. The agent doesn't decide who handles what. The matrix decides. Routing by inference works until it doesn't, and when it fails, you don't notice. 2. Parallel agents for tasks that split Spawn a procurement agent for pricing and a research agent for due diligence at the same time. You get richer output in the same elapsed time. Don't serialize what doesn't need to be serial. 3. Structured handoffs between agents When one agent finishes, it passes a brief to the next. "Analysis is complete. Key finding is X. Risks are Y. Your job is Z." That's context transfer, not just task completion. Without a structured handoff, every agent in the chain starts cold and repeats work the previous one already did. 4. A catch-all fallback with logging When no specialist matches the request, a general-purpose agent handles it. Log every catch-all request. That log is a map of gaps in your specialist coverage and doubles as your development backlog for which agents to build next. Start with the dispatch matrix. Map your five most common request types to the right agent, add a catch-all row at the bottom, and route by pattern from day one.