The Tool Desk
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You can add a practical verification loop to an existing AI agent in about 30 minutes without rebuilding its core prompt or tools. The pattern is a bounded outer loop: make the agent return a structured result, run deterministic checks on that result and any proposed tool calls, send concise failures back for repair, and stop after a small retry limit. Consequential actions such as sending, deleting, purchasing, publishing, or editing records should pause for human approval. Logging every attempt and saving a few representative cases turns the loop into a measurable safety and quality control rather than an opaque second opinion.
What is an AI-agent verification loop, and why add one?
A verification loop is a controller around your existing agent. The agent produces an answer or an action plan; a separate verifier checks it against explicit rules; the controller either returns the approved result, asks the agent to repair specific failures, or escalates to a person. The verifier is not merely another prompt saying “check your work.” It has a machine-readable contract, a terminal state, and a retry budget.
This separation matters because an agent can produce fluent but unsupported claims, malformed tool arguments, an unsafe action, or a response that never actually changed the requested state. A loop lets you test those failure modes before the result reaches a user or a side-effecting system. It also gives you an audit trail: input, output, checks, repairs, tool calls, and the final disposition.
Anthropic’s tool-use documentation describes the control-flow shape as a while loop keyed on stop_reason: request a response, execute approved tools, submit tool results, and continue while work remains. OpenAI’s Agents SDK similarly runs an agent loop until a final output is generated. A verification layer can sit around either pattern without changing the agent’s internal reasoning.
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What should the agent return before verification?
Start by defining a small output contract. Keep prose, evidence, and proposed side effects in separate fields so a checker can inspect them without trying to parse a paragraph.
{
"answer": "string",
"claims": [
{
"text": "string",
"evidence": ["document-123", "tool-result-7"],
"status": "supported"
}
],
"proposed_actions": [
{
"name": "update_ticket",
"arguments": {"id": "T-42", "priority": "high"},
"requires_approval": true
}
],
"evidence": [
{"id": "document-123", "type": "document", "location": "page 4"}
],
"confidence": 0.78
}Require each material claim to point to evidence or to carry an explicit unknown status. Do not let the model hide uncertainty in a confidence number alone; a numerical score does not identify which statement is uncertain. Keep proposed actions out of the natural-language answer, because a separate action list can be checked against an allowlist and an approval policy.
Define the terminal states
- Approved: all required checks pass and no pending side effect needs a person.
- Needs human review: a policy or business rule requires approval, or a check depends on subjective judgment.
- Verification failure: the retry cap, time limit, or token budget was reached without a valid result.
Make these states part of your API response. A caller should never have to infer from a vague sentence whether the agent completed work, needs approval, or gave up.
How do I add the loop in 30 minutes?
The fastest implementation is a thin controller around the agent you already have. The following schedule assumes your agent can accept a task and optional repair context, and that you can record a trace entry.
Minutes 0–5: write the contract
- List the fields the caller must receive: answer, claims, evidence, proposed actions, and confidence are a useful baseline.
- Mark required versus optional fields. For example,
claimsmay be required for a research answer but empty for a simple formatting task. - Define allowed values such as
supported,unknown, andcontradictedfor claim status. - Specify what counts as evidence: a document identifier, tool-result identifier, database row, or another stable reference.
- Write an explicit policy for side effects. Sending a message, deleting data, buying something, publishing content, or editing a record should normally set
requires_approvalto true.
Use JSON Schema, a typed model, or an equivalent validator so malformed output fails before business logic runs. Keep the schema versioned; changing a field or an allowed action is an API change for every verifier and client.
Minutes 5–12: implement deterministic checks
Run cheap, reproducible checks first. They catch failures that do not require a second language model and make repair messages precise.
| Check | What it verifies | Typical failure |
|---|---|---|
| Schema and required fields | JSON parses and every required field has the correct type | confidence is a string or claims is missing |
| Evidence presence | Every material claim has a valid evidence ID or an explicit unknown state | A numerical claim has no source reference |
| Evidence alignment | Referenced documents, tool results, or rows actually exist and support the claim | The cited record contains a different value |
| Action allowlist | Each proposed tool name is permitted for this task | The model invents an administrative tool |
| Argument types and ranges | Arguments conform to the tool schema and business limits | A negative quantity or an invalid record ID |
| Policy rules | Approval, privacy, authorization, and environment rules are satisfied | A production edit is proposed without approval |
| State change | The requested file, record, or artifact changed as intended | The tool returned success but the record is unchanged |
| Termination budget | Attempts, elapsed time, and token or cost budget remain within limits | A repair loop continues after its deadline |
Return a result such as {"ok":false,"failures":[...],"severity":"repair"}. Each failure should include a stable code, a short explanation, and the field or action that needs repair. Distinguish repair from needs_human; otherwise the agent may repeatedly try to automate a decision your policy reserves for a person.
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Minutes 12–20: wrap the agent in a bounded outer loop
Run the agent once, verify the structured result, and pass only the failure list back when a repair is possible. Do not replay the entire conversation blindly: long transcripts increase cost and can cause the model to repeat an earlier mistake.
failures = []
for attempt in range(3):
result = agent.run(task, prior_failures=failures)
check = verify(result)
trace.record(
attempt=attempt,
result=result,
check=check
)
if check.ok:
return result
if check.severity == "needs_human":
return request_human_review(result, check.failures)
failures = check.failures
return VerificationFailure(
failures=failures,
attempts=3
)Three total attempts is a sensible starting point, not a universal constant. A deterministic formatting repair may need only one retry; a multi-tool workflow may justify two repairs if its time budget allows. The important properties are a hard cap and a final failure state. Never let “try again until it passes” become an unbounded self-correction loop.
In a tool-using agent, the controller should verify each proposed call before execution, execute only approved calls, submit their results, and then verify the resulting state. The loop therefore has two gates: one before a side effect and one after it.
Minutes 20–25: gate side effects and human review
Separate automatic checks from approval decisions. OpenAI’s guardrail guidance recommends automatic guardrails for checks and human review for approval decisions. A verifier can confirm that an action is syntactically valid and allowed, but it should not silently authorize a consequential operation merely because the model expressed high confidence.
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- Read-only calls: validate the tool and arguments, then execute if the agent is authorized.
- Reversible writes: create a preview or draft and request approval before committing.
- Irreversible or high-impact writes: stop immediately for a named reviewer, with the exact action, arguments, evidence, and reason.
Make approval specific. “Approve agent action” is weaker than “Approve updating ticket T-42 from normal to high priority.” Include an expiration time, the identity of the approver, and the version of the policy used. If the underlying record changes while approval is pending, re-run the relevant checks before committing.
Minutes 25–30: log attempts and seed evaluations
Record enough context to reconstruct what happened without storing secrets unnecessarily:
- request ID, task input, model and prompt version;
- each attempt’s structured output and verifier result;
- failure codes, severity, retry count, elapsed time, and token or cost usage;
- tool names, sanitized arguments, tool results, and state-change observations;
- human reviewer, decision, timestamp, and final disposition.
Save three to ten representative cases as a small regression dataset. Include a normal success, an unsupported claim, a malformed action, and a case that must escalate. Re-run these cases after a prompt, model, tool, schema, or policy change. OpenAI’s tracing guidance emphasizes traces for debugging, while its evaluation guidance recommends datasets and repeatable evaluation runs; together they turn a one-off safety patch into an observable test cycle.
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What exactly should the verifier check?
Use separate checks for the answer, the evidence, the tool trajectory, and the resulting state. A single pass/fail score hides which layer failed and makes repair less reliable.
Answer correctness and uncertainty
Check that required facts are present, unsupported claims are absent, and uncertainty is explicit. For a policy or knowledge task, compare claims with an authoritative document or database value. If the available evidence is incomplete, require unknown rather than allowing the agent to fill the gap from plausible-sounding text.
Evidence alignment
Check both existence and meaning. An evidence ID that resolves to a document is not enough if the document does not support the associated claim. For structured data, compare the claimed value with the exact row or field; for documents, store a page, section, or span when possible.
Tool safety and trajectory
Validate the tool name, argument schema, authorization scope, and sequence. A tool may be individually allowed but unsafe after an earlier action, so inspect the trajectory as well as the final call. LangChain’s evaluation guidance separates run-level checks from trace- and thread-level checks and recommends examining the final response, trajectory, and state changes.
State change
When the task asks the agent to modify something, verify the postcondition independently. Read the record back, compare a file hash, or inspect the published artifact. Do not treat a tool’s “success” message as proof that the requested change occurred.
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Enforce maximum attempts, wall-clock time, and token or cost budgets in the controller rather than in the prompt. If a timeout occurs during a side effect, use an idempotency key and reconcile the external state before retrying; otherwise a repair attempt can duplicate a purchase, message, or update.
How should retry and repair behavior work?
Send concise, actionable failures, not the verifier’s entire internal trace. A repair message can state: “Claim 2 cites document-123, but that document does not contain the stated date. Replace the claim with the supported date or mark it unknown.” This gives the agent a bounded correction target.
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Keep the original task and successful evidence available, but do not ask the model to regenerate everything if only one field failed. For example, preserve an approved read-only tool result while requesting a replacement claim. If a repair would alter a previously approved side effect, invalidate the approval and require a new review.
| Failure type | Recommended response |
|---|---|
| Malformed JSON or missing field | One schema-focused repair; reject if still invalid |
| Missing or mismatched evidence | Request corrected citation or explicit unknown; do not invent a source |
| Disallowed tool or argument | Block the call and ask for an allowed alternative |
| Policy or authorization violation | Escalate to a human; further model retries cannot grant permission |
| Postcondition failure | Reconcile external state, then retry only if the operation is idempotent |
| Timeout or budget exhaustion | Stop with a verification failure and preserve the trace |
Use deterministic checks before subjective grading. If a check requires judging tone, usefulness, or nuanced factual quality, isolate it as a separately graded evaluation. Do not silently convert a subjective score into a hard fact or an authorization decision.
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Which verification design fits my agent?
Choose the narrowest design that covers the risk. More elaborate checks improve coverage but add latency, cost, and operational complexity.
| Design | Best for | Strength | Trade-off |
|---|---|---|---|
| Run-level verifier | One final answer or tool call | Fast, inexpensive, easy to debug | May miss errors in earlier steps |
| Trace-level verifier | Multi-step tool workflows | Checks ordering, intermediate calls, and policy transitions | More telemetry and storage are required |
| Thread or state-level verifier | Long-lived agents and records | Confirms cumulative state and cross-turn consistency | Harder to define and more expensive to run |
| Human-gated verifier | High-impact or irreversible actions | Places approval with an accountable person | Adds waiting time and review workload |
Compare alternatives on verification target, evidence strictness, retry and timeout policy, latency and cost, observability detail, and escalation rules. A run-level schema check may be sufficient for a read-only formatter. An agent that edits customer records needs action-policy checks and a postcondition check, and may also need trace-level review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I troubleshoot a loop that still fails?
The agent keeps returning the same failure
Inspect the failure message and the repair input. If the verifier reports a vague “quality” error, replace it with a field-level code and an example of the required form. If the model cannot satisfy a rule because evidence is unavailable, route that condition to unknown or human review instead of increasing the retry count.
Retries create duplicate side effects
Move side-effecting calls behind the approval gate, add idempotency keys, and reconcile the external state after any timeout. Never replay a write merely because the model did not receive a response; the first call may have succeeded.
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Run schema, allowlist, and argument checks locally before invoking a model-based grader. Cache immutable evidence lookups, parallelize independent read-only checks, and reserve trace-level or subjective grading for cases that need it. Set a wall-clock deadline so a safe failure is preferable to an indefinitely waiting user.
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The verifier rejects valid answers
Review false positives in the regression dataset. Common causes are an overly strict evidence rule, a schema that cannot represent legitimate uncertainty, or a state check performed before eventual consistency has settled. Version the verifier and policy, and record which version produced each decision so you can compare changes.
Logs contain sensitive information
Redact credentials, payment data, and unnecessary personal information before recording traces. Store references to large documents rather than copying them into every attempt, and apply retention limits appropriate to your data policy. Human reviewers should see the minimum context needed to make the approval decision.
What is the smallest production checklist?
Before enabling the loop for real users, confirm each item:
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- The agent emits a versioned, parseable schema.
- Every material claim has evidence or an explicit unknown state.
- Allowed tools and argument schemas are enforced outside the model.
- Side effects have a documented approval policy and an idempotency strategy.
- The controller has a retry cap, timeout, and token or cost budget.
- Repair messages contain only the failures needed for correction.
- Postconditions are checked for writes and other requested state changes.
- Each attempt, tool call, verifier result, and final disposition is traceable.
- A regression dataset covers success, repair, escalation, and terminal failure.
- Alerts distinguish an ordinary repair from a policy violation or system outage.
Where can I go deeper on agent evaluation?
For a broader treatment of evaluation sets, templates, metrics, evaluators, automated testing, and evaluation flywheels, Springer lists Reliable Evaluations for LLMs and AI Agents with a 2026 softcover edition and 193 pages. It is a reference for extending a small regression dataset into a repeatable evaluation program; it is not a prerequisite for adding the loop described here.
Vendor SDKs and guardrail interfaces change quickly. Recheck the exact method names, stop conditions, approval hooks, and tracing APIs for the SDK version you deploy. Keep the controller’s contract and policy tests independent of those SDK details so an upgrade does not silently remove verification.
What should I remember when I ship the loop?
A useful verification loop is small and explicit: structured output, deterministic checks, concise repair feedback, bounded retries, human approval for consequential actions, and traces that support repeatable evaluation. Add those controls around the agent you have today, then expand from run-level checks to trace and state checks only when your failures show that broader coverage is needed.
Frequently Asked Questions
Can a verification loop replace testing my agent before launch?
No. The loop protects individual production runs, while pre-release tests reveal systematic failures across a representative task set. Use both: run the verifier on a regression dataset and inspect cases that repeatedly need repair or escalation.
Should I use another language model as the verifier?
Only for checks that cannot be expressed reliably as code, such as nuanced relevance or tone. Run schema, evidence-ID, authorization, argument, and state checks deterministically first, and keep any model-based grader separate from approval logic.
How can I prevent a verifier from becoming a single point of failure?
Give the verifier its own timeout, version, and health metrics. If it is unavailable, fail closed for side effects and return a clear unavailable status for read-only work rather than allowing the agent to bypass verification.
What data should I include in a human approval screen?
Show the exact proposed action and arguments, the evidence supporting it, the affected record or resource, the policy rule that triggered review, and any relevant prior state. Include an approval expiry and require a fresh check if the resource changes before the decision is committed.
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