video1.0<iframe src="https://www.loom.com/embed/ba321bf179284a1980dd6280255d8843" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/ba321bf179284a1980dd6280255d8843-542e74bd0505095e.gif244.889In this video, I discuss the challenges we face in making sense of the vast amount of data generated by our AI agents, particularly for coding tasks. I demonstrate an application that analyzes our repository for MCP recommendations, highlighting the importance of context for our agents. I also show how we can improve our system by analyzing session data and creating sub-agents to enhance performance. I request viewers to consider the recommendations made and to review the pull requests generated for implementing these improvements. Overall, the goal is to optimize our processes and ensure our AI agents are functioning effectively.