GitHub says reliable multi-agent systems need schemas, actions, and MCP

In an X thread on March 9, 2026, GitHub resurfaced a guide on why multi-agent systems fail and what engineering patterns make them more reliable. The linked post itself was published on February 24, 2026, but the message is still timely: GitHub argues that most failures come from missing structure, not from raw model capability. Its framing is useful because it treats agent workflows less like chat interfaces and more like distributed systems with interfaces, contracts, and validation boundaries.

The first pattern is typed schemas. GitHub says multi-agent systems often break when agents exchange loosely structured natural language or drifting JSON. Field names change, types do not match, and downstream steps guess instead of validate. By forcing agents to emit machine-checkable data, teams can fail fast on invalid outputs and isolate problems to concrete contract violations instead of vague prompt behavior.

The second pattern is action schemas. GitHub argues that even when data shape is correct, intent can still be ambiguous. An instruction like “analyze this issue and help the team take action” may cause one agent to close the issue, another to assign it, and a third to do nothing. Action schemas reduce that ambiguity by defining a small, explicit set of permitted outcomes and requiring the agent to return exactly one valid action.

The third pattern is enforcement, which GitHub ties to Model Context Protocol, or MCP. In the post, MCP is described as the execution layer that validates tool inputs and outputs before calls run. That matters because conventions are not guarantees: schemas and allowed actions only help if the runtime enforces them consistently. GitHub’s point is that reliable agent systems depend on strict boundaries before state reaches production services.

The broader takeaway is that GitHub is pushing software-engineering discipline into agent orchestration. Instead of asking models to “be careful,” the company is recommending typed interfaces, constrained actions, and validated tool calls as default design choices. That guidance will resonate with teams building Copilot extensions, internal automations, or agent pipelines on top of MCP-compatible tooling, because it shifts the conversation from prompt cleverness to system design.