From memory to behavior: skills, user profiles, and the consolidation loop

Part 3 of the self-learning multi-agent series. Part 1 covered the tools and hooks. Part 2 introduced the semantic layer and memory Adding memory to an agent is fairly straightforward. Store something useful from a session, retrieve it when a similar task comes up, and include it in the next prompt. The agent no longer has to discover everything from scratch. After a few hundred sessions, though, the agent has a different problem. Imagine that twelve sessions produce twelve learnings about debugging the same hook pipeline. One explains how session deduplication works. Another records a cooldown that prevented a background job from running. A third captures the tool error that started the investigation. A similarity search might return five of them, leaving the agent to work out how they fit together each time. The individual learnings can all be useful while the collection remains difficult to use. We need a way to turn those fragments into a procedure. And once we have that procedure, we should stop injecting the fragments alongside it. This is the problem that the consolidation layer in the meta-knowledge-graph repository addresses. We want the debugging sessions to produce a reusable skill, with the steps that worked…