Self-Improving Agents That Teach the Company Back

Official Schedule Context

Agents forget too much. A run might solve a customer escalation, debug a deployment, or figure out

the review pattern for a tricky code path, then the knowledge disappears into a transcript. At

Runlayer, we started treating that knowledge as a product surface. Skills are reviewable, editable

instructions that agents can load over MCP. An agent can start with a task, learn something useful

while doing the work, and draft or update a private skill from that run. That skill loads into

future runs for the same agent, stays inspectable by humans, and can eventually graduate into a team

or org-level skill. The flywheel gets more interesting once a skill becomes useful beyond the agent

that created it. A learned skill can move from one agent's private memory into shared organizational

knowledge, then become available through the Runlayer plugin inside Claude Code, ChatGPT, and other

AI clients employees already use. The agent does the work, captures the playbook, and the company

gets better at that work everywhere agents are used. This talk walks through the architecture and

product choices behind self-improving skills: post-run distillation, skill mutation tools, private-

by-default scoping, runtime loading, UI inspection, promotion into shared skills, and the safety

boundary between this agent learned something and everyone should now use it. The goal is an agent

that leaves behind a better handbook for the next person, the next run, and eventually the whole

organization.

No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

Pending transcript synthesis when an official recording or confirmed matching video is available.