quality_assurance · education · workflow
Labelbox builds agentic evaluation benchmark for a leading AI lab
A leading AI lab needed a robust evaluation framework to test agentic models on complex, multi-step tool-use tasks — standard instruction-following assessments were insufficient for measuring planning, reasoning, and adaptation across real-world scenarios.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Define evaluation scope
The collaboration started by defining the scope: creating realistic simulations of common human-computer interactions.
Tools used
LabelboxPython
Outcome
The benchmark enabled the lab to pressure-test agentic performance across structured planning challenges, surface key gaps in reasoning and execution, and accelerate both product development and model iteration.
Results
Volume25
Grounding & classification
Source type: vendor customer story
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