# Confidence Reporting

> Surface the agent's uncertainty alongside its answer so downstream code and users know when to verify — not just what the answer is.

- **Category**: Agentic AI
- **Subcategory**: Verification & Reflection
- **Canonical URL**: https://designpattern.fyi/patterns/confidence_reporting/

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## Description
**Intent**: Give the system a usable signal for routing low-confidence answers to fallback or human review.

**Context**: Your assistant feeds into downstream decisions where acting on a wrong answer costs more than pausing to verify. The agent already produces answers; the question is how to attach a reliable uncertainty signal.

**Solution**: Produce a confidence label (high/medium/low or numeric) alongside each answer. Derive it from sample variance (self-consistency), evaluator score, retrieval recall, or rubric score. Render in UI; route low-confidence to fallback or human review.


## Use Cases
- Downstream code or UI needs to distinguish 'I know' from 'I'm guessing' on each answer.
- A confidence signal can be derived from sample variance, evaluator score, or retrieval recall.
- Low-confidence answers can be routed to fallback or human review usefully.





## Trade-offs


### Advantages

- Downstream code can branch on confidence — automated triage without human involvement.

- Users learn when to verify instead of trusting every answer equally.




### Considerations & Drawbacks

- Calibration is empirical and drifts as the model or task distribution changes.

- False confidence (model says 'high' but is wrong) remains the dangerous failure mode.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/confidence-reporting/)