Failure Disclosure
Failure disclosure is an AI UX pattern that clearly signals when the system could not answer, a tool failed, confidence is too low, or results are empty, instead of writing confident prose over a miss. Distinct error states help users recover and calibrate long-term trust.
When to use
Essential for AI search, agents, research tools, and any system where honest signaling of limitations, failed tool calls, and low-confidence outputs builds long-term user trust.
When not to use
- Transient loading moments better served by skeletons than failure chrome.
- Soft hedges on every answer when the system did succeed; reserve failure UI for real blocks.
- Internal-only tooling where structured logs matter more than end-user failure copy.
Anti-patterns
- Cheerful hallucination that invents sources or “completed” tool results.
- Generic “Something went wrong” with no cause or next step.
- Same visual style as a normal answer, so users miss the failure.
- Retry that loops forever without escalating or changing strategy.
How products use it
| Product | Implementation |
|---|---|
| Perplexity | States when search coverage is weak and leans on sources rather than invented claims. |
| Claude | Can decline or state uncertainty instead of fabricating when evidence is missing. |
| Cursor Agent | Surfaces blocked tool calls and failed steps in the agent trace. |
| ChatGPT | Shows clear tool or browsing failures rather than pretending the action succeeded. |
Use this pattern in your project
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "Failure Disclosure" AI interface design pattern.
Pattern Definition:Run · Apr 20 → Outcome
Run outcome
Apr 20, 2:14 PM
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Frequently asked questions
What is failure disclosure in AI UX?
Failure disclosure means the product visibly names when it cannot answer, a tool failed, or confidence is too low, instead of covering the miss with confident-sounding text. It is a core honesty pattern for long-term trust.
How is failure disclosure different from error recovery strategies?
Failure disclosure is how the UI signals the miss to the user. Error recovery strategies define what the system does next (retry, fallback, escalate). Good products combine both: clear signal plus a recovery path.
What should a failure state include?
State what failed in plain language, why if known (empty search, rate limit, blocked tool) and what the user can do next (rephrase, attach context, retry, or talk to a human).
Related patterns
- Trust
Progress Steps
Collapsible thinking and tool traces
- Trust
Citation Tooltips
Hover for source
- Trust
Confidence Score
Show how sure the model is about a claim
- Trust
Verification Next Steps
Concrete actions to validate uncertain output
- Trust
Knowledge Graph
Visualizing RAG
- Trust
Citations
Attach verifiable sources to generated claims