Multi-agent workflows often fail. Here’s how to engineer ones that don’t.

Multi-agent workflows in engineering, particularly at GitHub, often encounter failures due to implicit assumptions about state, ordering, and validation among agents handling related tasks. These systems behave more like distributed systems than chat interfaces, requiring explicit instructions, data formats, and interfaces to function reliably. Key failure points include messy natural language exchanges, vague intent, and loose interfaces, which can be mitigated through typed schemas, action schemas, and the Model Context Protocol (MCP). Typed schemas ensure data consistency, action schemas clarify permissible actions, and MCP enforces these structures as contracts to prevent errors. By treating agents as components of a distributed system and using structured interfaces, multi-agent systems become more reliable and deterministic, allowing them to handle tasks like codebase maintenance, automated code quality checks, and issue triage effectively.

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