# ClosedLoop AI

> ClosedLoop AI builds the feedback loop for AI agents: every production run is observed, evaluated, and turned into a tested improvement (observe → evaluate → improve → redeploy), so agents get measurably better instead of repeating mistakes. Founded by Pushpendra Chauhan. Contact: https://closedloopai.co/#contact

## Blog
- [What Is Closed-Loop AI? Why the Next Wave of Agents Will Learn From Every Run](https://closedloopai.co/blog/what-is-closed-loop-ai/): Closed-loop AI means agents that feed their outcomes back into their own improvement. Here's what that actually means, why it's rare, and why it's about to matter.
- [Why AI Agents Don't Learn From Their Mistakes](https://closedloopai.co/blog/why-ai-agents-dont-learn-from-mistakes/): LLMs are frozen at training time, and the systems we build around them rarely compensate. A breakdown of the four missing mechanisms that keep agents repeating the same failures.
- [Silent Degradation: How Production AI Agents Get Worse Without Anyone Noticing](https://closedloopai.co/blog/silent-degradation-production-ai-agents/): AI agents don't fail loudly — they drift. Model updates, data shift, and prompt rot degrade quality gradually, and without continuous evaluation nobody sees it until a customer does.
- [The Babysitter Problem: Why Human-in-the-Loop Doesn't Scale](https://closedloopai.co/blog/human-in-the-loop-doesnt-scale/): Human review is the right training wheel for AI agents and the wrong permanent architecture. The math of review queues, and what humans should actually be doing in the loop.
- [Open-Loop vs Closed-Loop AI Agents: A Control-Theory View](https://closedloopai.co/blog/open-loop-vs-closed-loop-ai-agents/): Control engineers solved the open-vs-closed-loop question eighty years ago. Applying the same lens to AI agents explains exactly why "deploy and hope" fails and what feedback must flow where.
- [The Real Cost of an AI Agent That Repeats Its Mistakes](https://closedloopai.co/blog/cost-of-repeated-agent-failures/): A failure that never teaches the system anything is a recurring subscription you never cancel. The unit economics of repeated agent failures, with the math that makes the case for feedback loops.
- [Evals Are Not Enough: Evaluation Without Action Is Expensive Logging](https://closedloopai.co/blog/evals-are-not-enough/): The AI industry finally embraced evals — and then stopped. Scores that don't trigger changes are dashboards, not feedback. What it takes to connect measurement to improvement.
- [How to Build a Feedback Loop for AI Agents: A Practical Architecture](https://closedloopai.co/blog/how-to-build-feedback-loop-ai-agents/): A concrete, component-by-component architecture for closing the loop on production AI agents — capture, outcome joins, evaluation, clustering, candidate generation, and gated promotion.
- [LLM-as-Judge in Production: Rubrics That Correlate With Real Outcomes](https://closedloopai.co/blog/llm-as-judge-production-guide/): LLM-as-judge is the only evaluation method that scales to judgment calls — and the easiest to do badly. Rubric design, calibration against outcomes, and the failure modes that produce confident nonsense scores.
- [Regression Gates: How to Ship Prompt Changes Without Breaking Your Agent](https://closedloopai.co/blog/regression-gates-prompt-changes/): Prompt changes are deploys without tests in most teams — every fix a gamble. How to build the CI/CD discipline for agent behavior: golden sets, failure archives, and promotion criteria.
- [Failure Clustering: Turn Thousands of Agent Errors Into Five Fixable Patterns](https://closedloopai.co/blog/failure-clustering-agent-errors/): Individual agent failures are anecdotes; clusters are engineering problems. How to group, label, and rank agent failures so the improvement backlog writes itself.
- [Outcome Data Is the Moat: What the Agent Observability Stack Is Missing](https://closedloopai.co/blog/outcome-data-is-the-moat/): The observability industry captured every token of what agents say and almost nothing about what agents cause. Why outcome data is both the missing signal and the durable advantage.