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Yes—you can add a useful verification loop to an existing AI agent in about 30 minutes without rebuilding its core prompt or tools. Wrap the agent in a bounded controller that (1) requires a structured result, (2) runs deterministic checks, (3) returns only concise failures for repair, (4) pauses before consequential side effects, and (5) records every attempt. One or two repairs are usually enough; a hard retry cap and an explicit human-review path prevent an endless self-correction cycle.
The loop improves reliability by checking the answer, the evidence behind it, the proposed tool call, and the resulting state separately. It does not prove that an answer is true, so subjective quality grading should remain a separate evaluation step.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhat is a verification loop for an AI agent?
A verification loop is an outer control loop around an agent run. The agent produces a candidate answer or action, a verifier evaluates that candidate against explicit rules, and the controller either accepts it, requests a focused repair, escalates to a person, or stops with a failure.
This is different from asking a model to “check your work” inside the same prompt. A separate verifier has its own input, output format, and stopping policy. Deterministic checks can reject malformed JSON, missing evidence, disallowed tools, or invalid arguments before a second model call is made. A separate quality grader can then assess harder questions such as completeness or tone.
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Anthropic’s documented tool-loop pattern is a request, tool execution, result submission, and continuation while work remains; its canonical shape is a while loop keyed on stop_reason. An agent verification loop adds a validation gate around that process rather than changing the agent’s internal reasoning.
What should the 30-minute implementation contain?
| Time | Build | Done when |
|---|---|---|
| Minutes 0–5 | Output contract | The agent returns an object with an answer, claims, proposed actions, evidence, and confidence. Every claim has evidence or an explicit unknown state. |
| Minutes 5–12 | Deterministic verifier | Required fields, schema, evidence identifiers, allowed tools, argument types, and business rules produce {ok, failures[], severity}. |
| Minutes 12–20 | Bounded outer loop | A failed result is repaired using only the machine-readable failure list, with a maximum of one or two retries. |
| Minutes 20–25 | Side-effect gate | Sending, deleting, purchasing, publishing, or record edits require policy approval or human review. |
| Minutes 25–30 | Tracing and seed evals | Each attempt is logged, and three to ten representative cases are saved for regression runs. |
The schedule is a practical first pass, not a performance guarantee. SDK methods, guardrail interfaces, and model behavior change, so verify the exact interfaces for the version you deploy.
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How do I define an output contract in the first five minutes?
Start by replacing free-form text at the agent boundary with a strict, versioned object. Keep the user-facing prose separate from claims and proposed actions so each part can be checked independently.
Use a small, explicit schema
{
'answer': 'string',
'claims': [
{
'text': 'string',
'evidence': ['document:abc', 'tool:orders:42'],
'uncertainty': 'known|unknown|estimated'
}
],
'proposed_actions': [
{
'tool': 'string',
'arguments': {},
'requires_approval': true
}
],
'evidence': [
{
'id': 'string',
'source_type': 'document|tool_result|database',
'locator': 'string'
}
],
'confidence': 0.0
}Use your SDK’s structured-output or JSON-schema facility when available. Otherwise, parse the response, reject invalid JSON, and send a repair request instead of attempting to infer missing fields from prose. Version the schema so a later field change does not silently invalidate old traces.
Make uncertainty a valid state
Require each material claim to point to an evidence identifier or to state unknown. Do not let an empty evidence array mean both “the claim is unsupported” and “the claim is intentionally unknown”; those are different verifier outcomes. Keep confidence as a signal for triage, not as proof that a claim is correct.
Separate actions from explanation
A sentence such as “I will refund the order” must not itself trigger a refund. Put the tool name and typed arguments in proposed_actions, then apply an allowlist and approval policy to that field. The answer can describe what would happen while the controller decides whether anything may actually happen.
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Which deterministic checks should run between attempts?
Run cheap, machine-readable checks before subjective grading. Return all failures from one pass so the agent can repair several issues at once rather than consuming a retry for each error.
Schema and required-field checks
- Parse the response as JSON and validate it against the current schema version.
- Require non-empty values for fields needed by the task.
- Confirm that
confidenceis numeric and within the range your contract defines. - Reject unknown top-level keys if strict compatibility matters, or record them as warnings if forward compatibility is preferred.
Evidence-alignment checks
- Verify that every material claim has at least one evidence identifier or an explicit
unknownstate. - Check that each identifier resolves to a document, tool result, or database row available in the current run.
- Flag claims whose wording exceeds the fields supported by the cited evidence. A row containing a date does not support a claim about a person’s motive.
Tool and policy checks
- Allow only registered tool names; reject spelling variations and unregistered destinations.
- Validate argument types, required arguments, enum values, and ranges with the same schema used by the tool.
- Mark side-effecting tools for approval even when their arguments are valid.
- Apply simple business rules such as ownership, amount limits, tenant boundaries, and date windows before execution.
Termination checks
- Track attempts, elapsed time, and token or cost budget.
- Stop if the agent returns a final answer with no proposed action, if the verifier passes, or if a policy failure requires a person.
- Never treat a timeout or an exhausted budget as success; return a distinct terminal failure.
Use {ok, failures[], severity} as the verifier result. Each failure should contain a stable code, a short explanation, the affected field or claim, and a repair hint. For example: {'code':'EVIDENCE_MISSING','path':'claims[1]','message':'Add an evidence id or mark this claim unknown'}.
How do I add the bounded repair loop?
Run the agent once, verify the structured result, and pass only the failure list back for repair. Keep the original task and relevant context available, but do not replay an ever-growing transcript of failed attempts.
- Initialize
failuresas an empty list and set a fixed maximum attempt count, such as three total runs. - Call the agent with the task and the current failure list. On the first run the list is empty.
- Run deterministic verification and record the result before any repair call.
- If
okis true, continue to the side-effect policy gate or return the answer. - If severity is
needs_human, stop and present the result and failures for review. - Otherwise, replace
failureswith the new concise list and retry while attempts remain. - When the cap is reached, return a typed verification failure containing the final failures and attempt count.
failures = []
max_attempts = 3
for attempt in range(max_attempts):
result = agent.run(task, prior_failures=failures)
check = verify(result)
trace.record(
attempt=attempt + 1,
result=result,
check=check
)
if check['ok']:
if policy_requires_approval(result):
return request_human_review(result, check['failures'])
return result
if check['severity'] == 'needs_human':
return request_human_review(result, check['failures'])
failures = check['failures']
return VerificationFailure(
failures=failures,
attempts=max_attempts
)The retry cap prevents an unbounded self-correction loop. The verifier should be cheaper and more deterministic than the main agent wherever possible. If a check needs subjective judgment, isolate it as a separately graded evaluation instead of silently treating its score as a factual pass.
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Why send failures instead of the whole conversation?
A short failure list gives the model an actionable repair target and keeps token use predictable. Replaying every prior message can preserve the original mistake, increase latency, and make it unclear which requirement is still unsatisfied. Retain the full transcript in tracing for diagnosis, but send the repair controller only the fields it needs.
How do I gate consequential tool calls?
Validation is not authorization. A tool call can be perfectly formed and still be unsafe or outside the user’s authority. Put a policy decision between verification and execution for actions that send, delete, purchase, publish, or edit records.
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Classify tools by effect
| Class | Examples | Controller behavior |
|---|---|---|
| Read-only | Search, retrieve, calculate | Execute after schema and access checks. |
| Reversible write | Create a draft, stage a change | Execute only in a sandbox or with an explicit policy allowance. |
| Consequential write | Send, delete, purchase, publish, edit a live record | Require approval, a narrow authorization rule, or a human review queue. |
OpenAI guidance distinguishes automatic guardrails from human review for approval decisions: use guardrails for checks that can be evaluated consistently, and human review when a person must approve a sensitive side effect. Record who or what approved the action, which arguments were shown, and whether the final execution matched the approved payload.
Prevent approval bypasses
- Do not execute commands embedded in the natural-language answer.
- Re-run argument and policy checks immediately before execution; data may have changed during review.
- Bind an approval to a hash of the exact tool name and arguments, with an expiration time.
- Return the execution result to the verifier so it can check the requested state change.
What should I log and evaluate in the last five minutes?
Create one trace record per attempt and one final disposition per task. OpenAI’s tracing and evaluation guidance emphasizes traces for debugging and datasets or evaluation runs for repeatability; LangChain’s evaluation guidance separates run-level checks from broader trace- and thread-level behavior.
Minimum trace fields
- Task identifier, schema version, model and prompt version, and timestamps.
- Input references and the structured result, with sensitive values redacted according to your retention policy.
- Verifier outcome, every failure code, severity, attempt number, and retry reason.
- Tool calls, arguments, approvals, execution results, and observed state changes.
- Final disposition: accepted, repaired, escalated, timed out, budget-exhausted, or failed.
Seed a small regression dataset
Save three to ten representative cases: a normal success, a missing-evidence response, a malformed action, a policy-sensitive request, and at least one case where the requested state does not change. Rerun them after prompt, model, tool, or verifier changes. Keep expected outcomes and failure codes explicit so a test can detect both false passes and unnecessary escalations.
Evaluate three layers
| Layer | Question | Typical cost |
|---|---|---|
| Run | Did this output satisfy the schema and local rules? | Low; usually deterministic. |
| Trace | Did the sequence of model and tool calls follow policy? | Moderate; requires the full attempt history. |
| Thread or task | Did the final answer, trajectory, and state change solve the user’s request? | Higher; may require a grader or human judgment. |
Do not collapse these layers into one score. A run can be well-formed while a trace violates a tool policy, and a compliant trace can still fail to produce the requested state change.
What exactly should a verifier check?
Use the verification target to choose the check, evidence requirement, and escalation policy. The following matrix keeps those decisions explicit.
| Target | Checks | Evidence standard | Typical response to failure |
|---|---|---|---|
| Answer | Required facts, unsupported claims, uncertainty, formatting | Each material claim maps to a source or is marked unknown | Repair the answer; escalate if a source conflict is unresolved |
| Tool call | Allowlist, argument schema, authorization, limits | Arguments derive from validated inputs and approved context | Repair arguments or request approval; never execute a disallowed call |
| State change | Target identity, before/after values, idempotency, completion | Tool result or database read confirms the intended change | Retry only when idempotency is guaranteed; otherwise investigate manually |
| Termination | Retry count, timeout, token and cost budget | Controller telemetry | Return a typed failure or human review, not a guessed answer |
How should retries, timeouts, and idempotency work?
Set independent limits for attempts, wall-clock time, and budget. A model may keep producing technically different outputs that fail the same rule, so a new attempt is not automatically progress.
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- Attempt cap: choose a small fixed maximum, such as three total runs in the example controller, and expose the count in the final error.
- Timeout: stop the entire task when its deadline expires, including waiting for tools or human approval.
- Budget: account for model tokens, tool charges, and any external API costs before starting another repair.
- Idempotency: attach an idempotency key to writes and verify the resulting state before considering a retry. Never blindly repeat an unknown-outcome purchase, deletion, or message send.
- Backoff: transient transport failures can use bounded backoff, but a policy or schema failure should be repaired immediately rather than retried as if the service were unavailable.
Use distinct terminal types so callers can decide what to do next: accepted, needs_human, verification_failed, timeout, and budget_exhausted. This prevents downstream code from treating every non-success as a prompt-repair opportunity.
Which verification-loop design should I choose?
Compare designs by what they verify, how strict their evidence rules are, their retry and timeout policy, observability, latency, and human-escalation behavior.
| Design | Strength | Trade-off | Best fit |
|---|---|---|---|
| Run-level deterministic checks | Fast, inexpensive, predictable | Cannot judge broad behavior or nuanced quality | Every production call for schema, evidence, and tool safety |
| Trace-level evaluation | Catches unsafe or inefficient trajectories | Needs complete logs and more processing | Regression testing and incident analysis |
| Thread/task-level grading | Measures final usefulness and state change | May require a model grader or human judgment | Complex multi-turn tasks and release gates |
| Human approval gate | Handles sensitive, ambiguous decisions | Adds latency and operational cost | Consequential side effects or unresolved evidence conflicts |
A practical deployment combines all four: deterministic checks on every run, trace and task evaluations on a regression set, and human review for defined policy classes.
What commonly breaks, and how do I fix it?
The agent keeps returning prose instead of the schema
Reject the response before any tool call, include the parser error and the exact missing path in the repair list, and keep the schema instruction adjacent to the requested output format. If failures persist, use a structured-output API or a small adapter that converts only validated fields.
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Separate identifier existence from semantic alignment. A deterministic pass can confirm that a source is present; a second grader or a rule tied to the source fields must decide whether the wording is supported. Mark unresolved conflicts as unknown rather than silently selecting one source.
Repairs oscillate between two failures
Detect repeated failure codes and paths across attempts. Stop early with a diagnostic when the same failure recurs, then escalate or adjust the contract; increasing the retry count usually adds cost without adding information.
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A tool succeeds but the requested record did not change
Verify state with a fresh read using the tool’s returned identifier. If the operation is not idempotent or the result is ambiguous, do not retry automatically; send the before/after evidence to a human.
Logs contain secrets or personal data
Redact at the trace boundary, store references instead of raw payloads where possible, and apply retention and access controls. A verifier cannot improve reliability if its logs create a larger security incident.
How do I maintain the loop as SDKs and models change?
Pin and record SDK, model, prompt, schema, and tool versions in every trace. Re-run the seed dataset after each change and inspect both false passes and false failures. Guardrail and runner interfaces evolve quickly, so check the current documentation before copying method names into production code.
For a deeper treatment of evaluation sets, templates, metrics, automated testing, and evaluation flywheels, Springer Nature lists the 2026 softcover edition of Reliable Evaluations for LLMs and AI Agents at 193 pages. Treat it as a reference for designing the evaluation program around your loop, not as a substitute for task-specific tests.
What should I remember before shipping?
- A verification loop is a bounded outer controller with an explicit verifier and terminal states.
- Run deterministic schema, evidence, policy, and argument checks before subjective quality grading.
- Return concise failure reasons for repair instead of replaying the entire conversation.
- Cap retries, enforce time and budget limits, and escalate consequential effects to a person.
- Trace every attempt and maintain a small regression dataset so changes are measurable.
Frequently Asked Questions
Can I add a verification loop without changing my agent’s prompt?
Usually, yes. Place the controller around the existing run, then add a response adapter or structured-output requirement at the boundary; the agent’s internal tools and prompt can remain unchanged initially. You will still need to teach the agent how to interpret the concise failure objects when a repair is requested.
Should the verifier be another language model?
Use deterministic code for parsing, schemas, allowlists, argument types, and simple business rules. A separate model grader is appropriate for semantic evidence alignment or overall quality, but keep its judgment isolated, labeled as subjective, and covered by a regression set.
How can I prevent duplicate actions after a retry?
Use an idempotency key derived from the task and intended action, and check the current state before execution. If the prior attempt has an unknown outcome, pause for a read-back or human review instead of issuing the write again.
What is the right retry count for a production agent?
There is no universal number; choose a small cap that fits the task’s latency and cost budget, then measure repeated failure codes in your traces. Increase the cap only when the regression data shows that additional attempts resolve distinct, recoverable failures rather than repeating the same defect.
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