Self-Improving Agents

Agents that improve themselves are showing up everywhere in this year’s program: agents that write their own skills, loops that rewrite the harness, models trained on their own verified outputs, and agents that learn at test time. But “self-improvement” can mean two very different things. One approach changes the model weights. The other keeps the weights frozen and improves the system around the model: prompts, tools, skills, memory, and the agent harness. Despite the difference, both face the same fundamental challenge: generating a change is cheap; knowing whether it actually made the system better is much harder. This social brings these communities together: researchers training self-improving models, engineers building self-improving agent systems, and practitioners running them in production. We’ll start with a short presentation featuring real-world data from a self-improving agent system running in production, followed by small-group discussions. The goal is to connect people approaching self-improvement from different directions, share practical lessons, and spark new collaborations