Autocontext is a harness for AI agents that runs iterative evaluation cycles against real tasks. It tracks what worked versus what failed, produces structured traces and artifacts, and optionally distills a local model from successful runs. The system integrates with multiple LLM providers including Claude, Codex, and a local Pi coding agent. Repeated executions improve agent performance over time rather than just varying behavior.