01
◇
Observe
Score and log requests without interrupting users.
Intent infrastructure for AI agents
Find where AI agents
misunderstand users.
IntentLayer detects post-run failures, clusters recurring intent gaps, and proposes narrow tenant-approved questions for those clusters.
Validated on 20,000 real conversations · measured546 confirmed misunderstanding events in our corpus · LLM-judgedRead methodology →
01 · Live playground
See what your agent is missing.
No account. No API key. Requests are scored locally by deterministic rules.
Intent analysis appears here
Run an example to inspect ambiguity, missing dimensions, questions, and executable intent.
02 · The loop
Observe first. Suggest narrowly.
Detection and measurement are shipped. Prevention remains beta and requires reviewed, tenant-approved patterns.
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02
⌁
Telemetry
Detect rephrases, corrections, regenerations, and abandoned responses.
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03
▦
Act
Generate evals and approve narrow questions for proven clusters.
03 · Why now
eligible English WildChat conversations scanned
measured full scanprecision for explicit corrections in a reviewed sample
26 of 33 · LLM-judged, not human-verifiedagentic detector F1 on a repository-disjoint split
P 0.63 · R 0.80Early access
See whether your agent has an intent problem.
Make it observable.
Working with a small group of teams deploying agents into real user workflows.