Fixing AI Agents to Prevent Failures in Production

The author shares insights on why AI agents fail in production, despite successful demos and evaluations. The key issues were not with the models themselves, but with architectural problems like tool call loops, context window mismanagement, lack of graceful fallback, and missing human checkpoints.

Details

The author spent three months observing AI agents failing in production for reasons unrelated to the models themselves. The key issues fell into four categories: tool call loops where agents got stuck in repetitive calls, context window mismanagement leading to irrelevant history crowding out crucial information, lack of graceful fallback causing agents to hallucinate completions instead of surfacing failures, and missing human checkpoints allowing single bad decisions to cascade into unrecoverable states. To address these problems, the author implemented architectural changes like explicit loop detection, sliding context windows to manage history, failure states to avoid guessing, and approval gates for critical actions. None of this required switching models - the same models performed dramatically better with the right infrastructure in place. The deeper lesson is that AI agents fail for the same reasons software fails in production: insufficient error handling, lack of observability, and overconfidence in the happy path. Treating AI agents like junior developers making autonomous API calls, with the same code review and safeguards, is key to preventing production failures.