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sonam pankajleft a comment
Most production agents share a common flaw: even with evals and observability in place, improvement still requires manual intervention. The retrieval layer gets plenty of attention, but the harder question of how to make agents genuinely outcome-driven gets overlooked. Agents have no sense of what a good trajectory looks like, and no memory of where they went wrong. Reinforcement learning...
ReflectSelf-Improving Layer Between Agent's Observability & Action
Production agent stacks have three components: observability, eval, and action. Your observability stack captures every tool call. Your eval suite judges whether the final output was correct. But the agent that runs tomorrow starts from a blank slate. The eval signal dies in a dashboard.
This is the missing RL layer:
Reflect sits between your evals and your agent. It treats traces not as passive audit logs, but as a training signal.
ReflectSelf-Improving Layer Between Agent's Observability & Action