AI Agents Do Weird Stuff (and how to fix it)

Developers face significant challenges when debugging AI agents due to their nondeterministic nature, which makes reproducing and fixing unexpected behavior difficult, especially in complex or long-running situations. One effective solution to this problem is the use of durable workflows, which checkpoint every step an agent takes by recording each LLM or tool call into a database, creating a durable and replayable trace of the agent's execution. This approach not only enhances observability by allowing developers to visualize an agent's activity and pinpoint failures but also facilitates reproducibility by enabling the restoration of an agent to any prior state, thereby allowing for iterative testing and debugging. By using durable workflows to impose determinism on AI agents, developers can more easily isolate and fix problematic steps, reducing the time and resources needed for troubleshooting.

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