agent-improvementlisted

# Agent-improvement loop An autonomous AI engineer improves the products it owns. This skill is how something improves **the engineer itself**, from the outside, on evidence the agent generated by running. **It complements a `self-improvement` skill rather than replacing it.** Self-improvement is a single run reflecting on its own memory, on a slow distil cadence. This is an **external observer** across the whole session corpus — and, in a deployment running more than one instance, across all of them at once. The observer's own Agent Improver sessions are part of that corpus too: it improves the Engineer's execution plane and its own observation plane as separately measured subjects. That vantage point is what makes it see things no single run can: - failures that recur across hundreds of runs but look like one-offs from inside any single run; - waste that feels normal from inside a run (a wait, a retry, a re-read) and is obvious in aggregate; - **divergence between sibling instances** of the same brain; - **drift between what the agent's bootstrap/loader says and what its contract says** — the loaders are typically not version-controlled, so they diverge silently and nothing else is looking; - **stale beliefs** in durable memory that quietly cause wrong action. **One line:** measure the agent from its own behaviour → find the pattern that costs most → fix it at the root, reversibly, with evidence → prove next run that the metric moved. --- ## The ingestion boundary —