Category Hub
Loop Engineering
Outcome loops, agent feedback, and the difference between an agent that remembers and one that improves.
Architecting autonomous outcome loops
Loop engineering focuses on turning generative AI agents into closed-loop execution engines. Explore how autonomous agents run explore/exploit cycles, verify external results, and update durable state from real business metrics.
Core Topics & Workflows
Articles in Loop Engineering
Agent reward loops: the missing layer between memory and real-world improvement
AI agents don't improve just because they remember. They improve when each shipped action is tied to measured reward and used in the next run.
Agent memory vs. agent feedback loops: why remembering isn't the same as improving
Most "AI agent memory" just gives an agent recall of the past. It doesn't make the agent better. Here's the difference between memory and a feedback loop.
Evals vs. outcomes: why your AI agent should loop on a real-world goal
Evals tell you if an agent passed a test. Outcomes tell you if it moved the number you actually care about. Here's the difference, and why it changes how you build.