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The fastest way to lose trust in an agent is to have it act confidently on a wrong answer. A verification step before the agent commits to an action turns a guess into a checked decision, and it is one of the cheapest reliability investments available once an agent is doing anything that matters.
The fastest way to lose trust in an agent is to have it act confidently on a wrong answer. A verification step before the agent commits to an action turns a guess into a checked decision, and it is one of the cheapest reliability investments available once an agent is doing anything that matters.
Verify Before You Act
A self-correcting agent adds a checkpoint between drafting a response and executing it. That checkpoint can be rule-based (does this output match the expected schema, does this field exist on the record) or model-based (a second pass that scores the draft against the original request). Either way, the agent is asked to grade its own work before it becomes visible to a user or writes anything to a record.
Rule-based checks are cheap and deterministic, and you should use them wherever the criteria are actually checkable in code: field exists, value is within an expected range, the record the agent is about to update is the one the user actually asked about. Model-based checks fill the gap where the criteria are fuzzier, like whether a drafted response actually answers the question the customer asked, not just a related question.
Two Kinds of Wrong
It helps to separate two failure modes the checkpoint needs to catch. The first is factually wrong: the agent states something that contradicts the data it retrieved. The second is contextually wrong: the answer is technically accurate but does not address what the user actually needed. Rule-based checks catch almost none of the second category. That is exactly where a model-based critique step earns its cost.
Implementing the Loop in AgentScript
In practice this means a bounded retry loop: draft, check, and if the check fails, revise with the specific reason attached rather than starting over blind. Cap the retries, usually two or three, and define an explicit fallback: escalate to a human, or return a clear "I could not verify this" instead of a silent wrong answer. The goal is not perfection on every attempt. It is making failure visible instead of confidently wrong.
Why the Reason Matters More Than the Retry
The detail that makes this actually work is passing the specific reason a check failed back into the revision step, not just a generic "try again." An agent told the account ID in its draft does not match the account the user asked about can fix that in one pass. An agent just told to retry with no reason often reproduces the same mistake, because nothing about its input actually changed. The critique has to be specific enough to act on, or the loop is just burning tokens without improving the outcome.
Conclusion
A feedback loop is a small addition to an agent's design and one of the highest-leverage ones. It is the difference between an agent that sounds right and one that has actually checked.
Start with the highest-stakes actions in your agent, the ones that write data or trigger something irreversible, and add a verification step there first. You do not need to instrument every topic on day one to get most of the benefit.