Agent Benchmarking
Repeatable task suites that measure whether an agent is getting more reliable from one version to the next.
NLP & Gen AI
Agentic AI
Trajectory annotation, tool-use evaluation, and task-completion scoring for autonomous AI agents.
Overview
An agent doesn't just produce an answer — it plans, decides which tool to reach for, acts, reads the result, and decides again, often over many steps. That structure is exactly what makes evaluating agents hard: one wrong turn early on quietly derails everything after it, and a final answer that looks fine can be the product of a broken path. Our evaluators assess the whole trajectory rather than just the endpoint — whether each tool call was the right one and correctly formed, whether the plan was sensible, whether the agent noticed and recovered when something went wrong, and whether the task was actually accomplished. What you get back is signal at the step level, not just a pass/fail on the outcome.
What's included
- ✓Step-by-step trajectory review with each action labeled as sound or flawed
- ✓Tool-call checking — right tool, right arguments, right moment
- ✓Outcome scoring against task rubrics, separating “looked done” from “was done”
- ✓Assessment of planning and reasoning quality across the full sequence
- ✓A structured catalogue of how agents fail, plus whether they recovered
- ✓Human-demonstrated correct runs for use in fine-tuning
Use cases
Tool-Use & Function-Calling Data
Verified examples of correct tool selection and call formatting to fine-tune function-calling behavior.
Workflow Automation Review
Human checking of production agent runs before the agent is trusted with higher-stakes or irreversible actions.
Reliability & Safety Evaluation
Structured scenarios that probe how an agent behaves at its edges — where it stalls, loops, or takes actions it shouldn't.
Frequently asked questions
Do you evaluate the whole agent trajectory or just the final answer?
The whole trajectory — each tool call, the plan behind it, and whether the agent recovered from mistakes — because a final answer that looks right can come from a broken path.
How do you check tool calls specifically?
We verify that the right tool was chosen, with correct arguments, at the right step — and tag where and how it went wrong when it didn't.
Can you produce repeatable benchmarks across model versions?
Yes — reproducible task suites that measure whether reliability is improving from one version to the next.
Can we start with a small pilot before committing?
Yes. Given how specialized agent evaluation is, a pilot is the best way to align on rubrics and scoring before scaling.
Ready to scale your agentic ai?
Start with a pilot batch — see the quality of the data before you commit.