14 posts tagged with "agent-architecture"

Most agent frameworks make a critical mistake early: they treat the agent as a function. You call it, it loops, it returns. That mental model works for demos. It falls apart the moment a real-world task runs for 45 minutes, hits a rate limit at step 23, and you have nothing to resume from.

A production agent runtime is not a function runner. It is an execution substrate — something closer to a process scheduler or a distributed workflow engine than a Python function. Getting this distinction right from the beginning determines whether your agent system handles failures gracefully or requires a human to hit retry.

Most teams that build multi-agent systems hit the same wall: the thing works in demos and falls apart in production. Not because they implemented the coordination protocol wrong. Because the protocol itself is the problem.

Multi-agent AI has an intuitive appeal. Complex tasks should be broken into parallel workstreams. Specialized agents should handle specialized work. The orchestrator ties it together and the whole becomes greater than the sum of its parts. This intuition is wrong — or more precisely, it's premature. The practical failure rates of multi-agent systems in production range from 41% to 86.7% across studied execution traces. That's not a tuning problem. That's a structural one.