Use case — Engineering
Find what's failing.
Know exactly where to fix it.
AI systems fail quietly. Wrong answers look normal, retrieval misses go unnoticed, agent steps silently skip. By the time someone reports a problem, it's been happening for a while. DataFramer sits above your observability stack, surfaces failures you weren't looking for, and traces each one to the source.
Why production AI quality is hard to watch
Bad answers don't look like errors.
Wrong, incomplete, or subtly off-domain responses pass through cleanly. No exception thrown, no metric spiked. Users experience the problem; your dashboards don't.
You don't know what to look for until you've already missed it.
Known failure patterns are easy enough to monitor. The unknown ones — edge cases you didn't anticipate, retrieval behavior you didn't test — tend to cause the most damage.
Root cause is hard to pin down.
A failure could come from the prompt, the retrieval step, the context window, a tool call, model behavior, or the workflow logic. Without a way to narrow it, every fix is a guess.
Fixes introduce regressions.
Without a dataset of the failures that triggered a fix, there's no way to verify it worked — or that it didn't break something else.
How DataFramer helps
From silent failure to diagnosed root cause.
Connect your observability stack
Link Langfuse or LangSmith and DataFramer starts pulling traces immediately, without replacing anything in your existing setup. You can also send data directly via the SDK — traces, user feedback, corrections, and ratings.
Ingest
Search for failures in plain language
Type what you're looking for, or pick from the Problem Library, which covers hallucinations, failed tool executions, looping agents, retrieval duplicates, incomplete answers, policy violations, and more. DataFramer scans your traces and groups matching results.
Discovery
Continuously monitor what matters
When a failure pattern is worth watching, save it. Saved findings update automatically as new matching traces arrive, so you always have a current view without re-running searches. Connect Slack alerts to get notified when a pattern spikes.
Tracking
Diagnose the root cause
For each finding, DataFramer narrows the failure to its source: prompt, retrieval, context, tool call, workflow step, model behavior, or missing business context. You're not guessing which layer broke.
Diagnosis
Turn failures into fixes you can validate
Route traced failures to expert review, build eval datasets from them, and test fixes against the real cases that caused the problem before anything ships.
Fix & validate
What you can find
Known patterns, unknown signals, and everything between.
Reliability
- Hallucinations
- Incomplete answers
- Ignored retrieval context
- Failed tool executions
- Broken workflows
Efficiency
- Expensive traces
- Slow traces
- Redundant tool calls
- Inefficient workflows
- Retrieval duplicates
Agent systems
- Looping agents
- Coordination failures
- Planner failures
- Routing mistakes
- Stuck workflows
- Agents ignoring instructions
- Agents inventing tools
Safety
- Prompt injection attempts
- Secret leakage
- Unsafe outputs
- Policy violations
Discovery
- Anomalies
- Regressions
- Suspicious traces
- Unknown issue patterns
Your own
- Describe anything in plain language. DataFramer finds traces that match, even patterns not in the library.
Stop finding out from your users.
Connect your observability stack and start finding failures in your existing traces today. Free, no card required.