Agents With Memory: Turning Execution Logs into Institutional Knowledge
Logs are where automation failures usually go to become archaeology. A business deploys an AI agent. The agent calls APIs, checks intermediate states, makes assumptions, retries after errors, occasionally succeeds by accident, and sometimes discovers a genuinely efficient route through a workflow. The full execution trace is stored somewhere. In theory, this is valuable evidence. In practice, it often becomes a swamp: too verbose for managers, too unstructured for engineers, and too raw for the next agent run. ...