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Agents respond to feedback — just not how you think. The fastest way to improve output quality over time? ✅ Reinforce what works ✅ Redirect what doesn’t ✅ Keep the feedback loop tight Here’s how smart feedback makes agents smarter 👇

Agents don’t “learn” like humans. But they do respond to signals in the prompt history — especially in long sessions. That means your feedback helps shape what comes next.

When something works, say so: “Nice — this covers the main use case. Let’s just handle the null edge case too.” This encourages continuation, not correction. It keeps momentum high — and quality stable.

You can also course-correct gently: “Almost right. But this loop doesn’t handle timezones. Try again with that in mind.” The agent takes the correction and retains the confidence. That balance matters.

Why this works: - Encouragement makes prompts more grounded - Corrections become more specific - The model anchors on what not to change It’s the difference between rewriting from scratch… vs. iterating like a teammate.