A tool returns 214KB of logs. The window overflows, the agent's answers get worse, and nothing throws an error. Another agent retries the same call fourteen times. A third freezes seventeen seconds on a slow tool and times out. None of them crash. They just quietly cost you money and accuracy. This talk walks through the fix for all three: keep large outputs out of the window and pull them back by reference, give the agent a clear done signal so fourteen calls drop to two, and hand a slow tool a tracking id so a seventeen-second freeze becomes under two. You leave with working code, an open source repo, and a simple rule for which fix each failure needs.
Elizabeth Fuentes is a Developer Advocate and AI engineer specializing in agent harness architecture. Her work spans context engineering, agent memory, security guardrails, and taking agents to production. Through hands-on demos she breaks complex concepts into simple, understandable pieces of code, helping developers build more efficient agents and democratizing the adoption of advanced AI for everyone.
