Find the patterns hiding in millions of frames.
Agentuor agents discover recurring patterns, ambiguous examples, and failure modes across large multimodal datasets — and translate them into recommendations a person can act on.
Agent intelligence is the layer that connects everything else. Agents continuously index episodes, labels, reviewer decisions, and evaluation results, then look for structure: clusters of ambiguous examples, guideline rules that are applied inconsistently, scenarios where models repeatedly fail. Findings are presented as ranked, explainable recommendations with confidence and provenance. Nothing changes in the dataset until a human approves it.
Pattern discovery
Cluster similar episodes, objects, and motions across modalities to reveal what your dataset is really made of.
Failure-mode analysis
Group model and annotation failures into named classes with representative examples.
Ambiguity detection
Surface frames where annotators disagree or hesitate, before they distort training.
Actionable recommendations
Every insight maps to a concrete action: relabel, clarify a guideline, collect a scenario, add to an eval suite.
Confidence and provenance
Recommendations show their evidence and certainty; approvals and rejections are logged.
Human in control
Configure which recommendations are shown, to whom, and what requires sign-off.
Step by step
Index
Agents build a multimodal index over episodes, labels, decisions, and results.
Analyze
Patterns, ambiguities, and failure modes are detected and scored.
Recommend
Ranked, explainable suggestions appear in the relevant workspace.
Approve and learn
Human decisions refine future recommendations for the dataset.
Common questions
Do agents change labels on their own?
No. All changes require explicit human approval.
How is confidence calculated?
From agreement across evidence sources and calibration against past reviewer decisions on your data.
Can we audit a recommendation later?
Yes. Every recommendation keeps its evidence, score, and the decision made on it.