Agentic & Feedback Loops: Reflection, Verification, and Self-Improvement
One-shot generation commits to its first answer. Feedback loops add the missing check: verify the output (ideally against tests or schemas, not vibes), write down what went wrong, and retry — then turn production failures into permanent regression tests.
01.The Problem: The First Draft Is the Only Draft
Ask a model to write a function. It writes it, sounds confident, and stops. Nobody — not the model, not you — checked whether it runs.
Worse, single-pass generation has a structural flaw beyond sloppiness: once a model commits to a trajectory, it rationalizes it. Wrong early steps don't trigger "wait, let me redo that" — they trigger elaborate self-justification, because every new token is conditioned on the earlier ones. The first answer is the answer.
Now picture the opposite workflow — how a careful human does the same job:
- Draft something.
- Check it against something real — run the code, validate the format, ask "did this actually answer the question?"
- If it fails, write down what went wrong and why.
- Redo it, reading your own note first.
Plainly, an agent needs
Someone whose job is saying "no, and here is why" — before the answer ships.
Three 2023 papers turned that common-sense loop into a technique, and by 2026 it is standard equipment in every agent framework. This topic is that loop: what to check with, when to stop, and how the same idea runs at two scales — inside one task, and across your whole product.
Nested Feedback Loops
Nested Feedback Loops
An inner loop self-corrects within one task using verifiers + reflection; an outer loop turns production traces into durable system improvements.
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