Agent benchmarking
Reproducible task suites that measure agent reliability across versions.
NLP & Gen AI
Agentic AI
Trajectory annotation, tool-use evaluation, and task-completion scoring for autonomous AI agents.
Overview
AI agents don't just answer — they plan, call tools, and act across many steps, and a single wrong step compounds into a failed task. Vidyut Data's evaluators annotate and score agent trajectories end to end: was each tool call correct, did the plan make sense, did the agent recover from errors, was the final outcome achieved? The result is step-level training signal and reliable benchmarks for agentic systems.
What's included
- ✓Step-level trajectory annotation and error tagging
- ✓Tool-call correctness and argument validation
- ✓Task-completion and outcome scoring against rubrics
- ✓Planning and reasoning quality evaluation
- ✓Failure-mode taxonomy and recovery analysis
- ✓Human demonstrations for agent fine-tuning
Use cases
Tool-use training data
Correct tool-call demonstrations for function-calling fine-tuning.
Workflow automation QA
Human review of production agent runs before high-stakes actions.
Safety evaluation
Red-team scenarios that probe agents for unsafe or runaway behavior.
How we work
1
Scope & pilot
We review your data, define the labeling guide together, and run a paid pilot batch so you can judge quality before scaling.
2
Team & calibrate
A dedicated, trained team ramps on your guidelines, benchmarked against gold-standard tasks until accuracy targets are hit.
3
Produce & QA
Production batches flow through multi-tier QA — consensus review, statistical sampling, and edge-case escalation.
4
Deliver & iterate
Data ships in your format with accuracy reports. Guidelines evolve with your model's failure cases.
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.