Why AI Coding Agents Lose Track Mid-Task and What One Tool Aims to Do About It

AI coding agents often appear to "forget" progress during long debugging sessions, but the root cause may be context pollution rather than model capability limits. As agents accumulate logs, stack traces, old errors, and completed investigation steps, the context window fills with outdated and irrelevant information. Even large context windows do not solve the problem if the content inside them is too noisy to be actionable. A developer has built an experimental tool called Tokenectomy that preprocesses environment output before passing it to the agent, filtering out noise while preserving relevant state. The goal is not to improve the underlying model but to ensure the information the agent receives is cleaner, more focused, and better suited to the current task.