An AI developer encountered issues with multi-step AI agents losing context after several tool calls, causing hallucinations and incorrect actions. The root cause was the stateless nature of model inferences between tool calls, where the model's memory is limited to the growing message history, which becomes noisy and large. The practical solution implemented was to externalize state into a structured 'scratchpad' object that the agent reads from and writes to between steps, preserving key facts and decisions explicitly. Additionally, compressing older tool outputs into summaries and validating state before critical tool calls improved reliability. This approach was applied to multiple agents, fixing context-loss bugs in typical 3-10 step workflows and improving agent reliability significantly.

May 25, 2026, 8:00 PM