---
title: "Output Verification Loop"
summary: "Verify LLM outputs by extracting individual claims, checking each against evidence sources, and returning per-claim trust scores before acting on the result."
status: "emerging"
category: "Reliability & Eval"
url: "https://agentic-patterns.com/patterns/output-verification-loop"
source: "https://aclanthology.org/2023.emnlp-main.741/"
authors: ["John Weston (@JohnnyTarrr)"]
tags: ["verification","hallucination-detection","trust-scores","fact-checking","multi-agent"]
signals: ["Agent output feeds into decisions or downstream agents","Hallucination risk is non-trivial","Compliance requires an audit trail"]
anti_signals: ["Output is purely creative with no factual claims","Latency budget under 500ms"]
updated_at: "2026-10-13"
---

# Output Verification Loop

> Verify LLM outputs by extracting individual claims, checking each against evidence sources, and returning per-claim trust scores before acting on the result.

## Use when

- Agent output feeds into decisions or downstream agents
- Hallucination risk is non-trivial
- Compliance requires an audit trail

## Avoid when

- Output is purely creative with no factual claims
- Latency budget under 500ms

## Problem

LLM agents confidently produce outputs that contain factual errors, hallucinated citations, or unsupported claims. In multi-agent pipelines the problem compounds: one agent's hallucination becomes another agent's input. Standard reflection loops catch stylistic issues but lack grounding against external evidence, so factual errors pass through unchallenged.

## Solution

Insert a verification step between generation and action. The step works in three phases:

1. **Claim extraction** -- decompose the LLM output into individual, atomic claims.
2. **Evidence retrieval** -- for each claim, retrieve supporting or contradicting evidence from authoritative sources (APIs, knowledge bases, RAG stores).
3. **Scoring** -- assign a per-claim trust score based on evidence alignment and return an aggregate confidence for the full output.

The agent (or orchestrator) uses the scores to decide whether to proceed, retry with a corrected prompt, or escalate to a human.

```pseudo
output = agent.generate(prompt)
result = verify(output, context)

if result.trust_score >= threshold:
    proceed(output)
else:
    retry_with_feedback(result.flagged_claims)
```

In multi-agent systems, run verification at each hand-off between agents so errors don't propagate through the pipeline. Verification receipts (signed, timestamped results) provide an audit trail for compliance.

## How to use it

- Place a verification call after any agent step that produces factual claims others will depend on.
- Set a trust-score threshold appropriate to the domain (higher for medical/financial, lower for exploratory research).
- In a swarm or multi-agent pipeline, verify at every agent boundary rather than only at the final output.
- Store verification receipts when you need a compliance audit trail.

### Known implementations

- [VeroQ Shield](https://github.com/veroq-ai/shield) -- claim-level verification with evidence chains, Python and TypeScript SDKs. Self-hosted Docker option for air-gapped deployments. An early-stage vendor project (a handful of commits, no releases, paid SaaS tiers from $24 to $399 per month); no affiliation between the pattern author and veroq-ai is disclosed.
- [OpenFactCheck](https://arxiv.org/abs/2405.05583) -- open-source library for the same claim-decomposition plus evidence-retrieval pipeline.

## Trade-offs

- **Pros:** Catches factual errors that reflection loops miss; provides quantified confidence rather than binary pass/fail; audit trail via receipts; composable across multi-agent pipelines.
- **Cons:** Adds latency per verification call; evidence quality bounds verification quality; requires an evidence source relevant to the domain.

## References

- [VeroQ Shield -- LLM output verification](https://github.com/veroq-ai/shield)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://aclanthology.org/2023.emnlp-main.741/) (Min et al., EMNLP 2023) -- established the decomposition-into-atomic-claims plus evidence-check lineage this pattern follows
- [OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs](https://arxiv.org/abs/2405.05583) (2024) -- open-source, maintained implementation of the pipeline
- [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.11366) -- related but lacks external evidence grounding
- [FacTool: Factuality Detection in Generative AI](https://arxiv.org/abs/2307.13528) -- academic approach to claim-level verification