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Why AI Agent Orchestration Needs Runtime Context Between Agents

Every multi-agent system depends on one agent handing its output to the next, and nothing in the architecture confirms that the handoff carried what it should have. Orchestration adds a failure surface that single-agent architecture doesn’t have: a point between every two agents where one has to trust that the other passed along everything it needed, unverified.

Why AI Agent Architecture Needs a Runtime Context Layer

Every AI agent architecture diagram shows the same five layers: perception, memory, reasoning, action, and feedback. Each layer assumes the one before it worked correctly, and none of them can confirm that once the agent runs against live production data. Runtime context is the sixth layer most designs leave out, and it’s the one that decides whether any of the other five can be trusted.