Reflect
Self-Improving Layer Between Agent's Observability & Action
Start new thread
sonam pankaj

Reflect - Self-Improving Layer Between Agent's Observability & Action

by•
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.

Add a comment

Replies

Best
sonam pankaj
Maker
📌
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 changes that. Give an agent the right outcome and trajectory signals, and you give it the foundation for self-improvement.