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
How do you guarantee quality foragentic ai?
Every project runs through multi-tier QA: annotators are benchmarked against gold-standard tasks before production, batches are statistically sampled against agreed accuracy targets, and ambiguous cases are escalated and documented in a living labeling guide. You receive accuracy reports with every delivery.
Can we start with a small pilot before committing?
Yes — we recommend it. A paid pilot batch on your real data lets you evaluate our quality, turnaround, and communication before scaling. Pilot learnings become the project's labeling guide.
How is our data kept secure?
Client data is encrypted in transit and at rest, access is limited to the assigned project team under NDAs, and we support VPN-restricted or client-hosted workflows where data cannot leave your environment. Retention and certified deletion terms are set per engagement.
What tools and output formats do you support?
We work in your annotation platform or ours, and deliver in the format your pipeline expects — COCO, YOLO, Pascal VOC, JSON, CSV, or a custom schema — with delivery via API, cloud bucket, or scheduled export.
Ready to scale youragentic ai?
Start with a pilot batch — see our quality on your data before you commit.