Quality assurance · Production

Labelbox builds agentic evaluation benchmark for a leading AI lab

The problem

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.

Workflow diagram · grounded in source
1
Define evaluation scope
trigger
“Our collaboration started by defining the scope: creating realistic simulations of common human-computer interactions, such as booking intricate travel arrangements or managing the nuanced process of retail returns”
2
Build domain environments
output
“we meticulously constructed multiple distinct domain environments. Each environment was a self-contained ecosystem, complete with its own database and uniquely designed API interfaces. These interfaces were robust, supporting many functi…”
3
Develop multi-step tasks
output
“For every single interface, we developed a high-volume of multi-step tasks. These tasks were crafted to rigorously test the agent's ability to: Interpret natural language instructions with precision”
4
Two-tiered human expertise
human_review
“A two-tiered human expertise model, with trainers defining API interfaces and tool schemas, and another group creating realistic task prompts and ground truth action plans, ensured high-quality data and evaluation”
5
Score and verify tasks
validation
“Each task was scored for difficulty and grounded in programmatically verifiable outcomes, helping the team surface key gaps in reasoning and execution”
6
Deliver benchmark
output
“This resulted in a benchmark of 25 interfaces, over 250 API functions, and more than 1,000 tool-use tasks across different domains”
Reported 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.

Reported metrics
API interfaces in benchmark25
API functions in benchmarkover 250
Tool-use tasks in benchmarkmore than 1,000
API functions per environment15-20
Show all 5 reported metrics
API interfaces in benchmark25
API functions in benchmarkover 250
tool-use tasks in benchmarkmore than 1,000
API functions per environment15-20
product development and model iteration speedaccelerating both product development and model iteration
Reported stack
LabelboxPython
Source
https://labelbox.com/customers/agentic-development-customer-story/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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.

What tools did this team use?

Labelbox, Python.

What results were reported?

API interfaces in benchmark: 25; API functions in benchmark: over 250; Tool-use tasks in benchmark: more than 1,000; API functions per environment: 15-20 (source-reported, not independently verified).

How is this quality assurance AI workflow structured?

Define evaluation scope → Build domain environments → Develop multi-step tasks → Two-tiered human expertise → Score and verify tasks → Deliver benchmark.

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