The ClosedLoop Blog

Notes on agents that learn.

Why AI agents repeat their mistakes, the architecture that fixes it, and what everyone else is missing. Written by an engineer who builds this for a living.

Problem

What Is Closed-Loop AI? Why the Next Wave of Agents Will Learn From Every Run

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.

· 4 min read
 Problem

Why AI Agents Don't Learn From Their 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.

· 3 min read
 Problem

Silent Degradation: How Production AI Agents Get Worse Without Anyone Noticing

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.

· 3 min read
 Problem

The Babysitter Problem: Why Human-in-the-Loop Doesn't 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.

· 3 min read
 Problem

Open-Loop vs Closed-Loop AI Agents: A Control-Theory View

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.

· 3 min read
 Problem

The Real Cost of an AI Agent That Repeats Its Mistakes

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.

· 3 min read
 Solution

Evals Are Not Enough: Evaluation Without Action Is Expensive Logging

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.

· 3 min read
 Solution

How to Build a Feedback Loop for AI Agents: A Practical Architecture

A concrete, component-by-component architecture for closing the loop on production AI agents — capture, outcome joins, evaluation, clustering, candidate generation, and gated promotion.

· 3 min read
 Solution

LLM-as-Judge in Production: Rubrics That Correlate With Real Outcomes

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.

· 3 min read
 Solution

Regression Gates: How to Ship Prompt Changes Without Breaking Your Agent

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.

· 3 min read
 Solution

Failure Clustering: Turn Thousands of Agent Errors Into Five Fixable Patterns

Individual agent failures are anecdotes; clusters are engineering problems. How to group, label, and rank agent failures so the improvement backlog writes itself.

· 3 min read
 Perspective

Outcome Data Is the Moat: What the Agent Observability Stack Is Missing

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.

· 3 min read