Cut the agent cost and improve the quality by evaluation.
Generic benchmarks can't capture your data, workflows, or internal agents. We score your agents step by step on your own datasets and production traces — so you see where they break, not just whether the final answer looks right.
- user"I lost VPN access after my password reset."
- 01tool
get_employee_profile(email)GEA - 02tool
search_tickets(user, "VPN")GEA - 03tool
search_tickets(user, "VPN")E fail · 3/3 judges Repeats step 02 with identical argumentsGEA - 04reply
"Your ticket INC-4821 is resolved."G fail · 2/3 judges No tool result mentions INC-4821GEA
Final reply
Reads as correct
Step-level verdict
2 defects in 4 steps
- G Groundedness3/4
- E Efficiency3/4
- A Goal alignment4/4
From a real engagementOn a real enterprise IT-support agent, the smart router cut invented facts to 11% of conversations, down from 15% with a fixed model. Wasted actions fell to 2%, from up to 7%. See how we measured it
- conversations
- 136
- actions judged
- 533
- labeled examples
- 0
01 Evaluation solutions
Know when your agents are ready — and when they change
Most monitoringlatency · cost
We measureIs the agent producing high-quality results?
- t0 · baseline
Measure performance and robustness
Evaluate agents and LLM-powered applications across large volumes of internal data, including production traces.
- t1 · change
Understand the impact of changes
Use our evaluation framework to compare results before and after changing a model, agent framework, or configuration.
- t2 · production
Track performance in production
Keep measuring quality as users, inputs, and use cases evolve.
What we measure on every step
Groundedness
Did the agent stick to the facts it retrieved — or invent names, dates, and ticket statuses?
Efficiency
No repeated lookups, no calls it did not need.
Adaptability
When a tool fails or returns nothing, the agent recovers instead of giving up.
Goal alignment
Every step serves what the user actually asked for.
02 Built around you
labeled examples needed to score 533 agent actions in our latest case study
Evaluation that fits your data and workflow
High-quality evaluation can be difficult to build and maintain. We help you:
Evaluate with little or no human-labeled data
Not enough time or specialist resources to label large datasets? We use evaluation methods that work without a single labeled example — or that make the most of a small set.
Continuously evaluate in production
Monitor quality against real usage and adapt your evaluation as your agents and use cases evolve.
Work safely with sensitive traces
Production traces contain personal data. We can evaluate inside your environment, work under your NDA, and redact PII before anything is judged.
03 Case study
A 5× cheaper engine, every trade-off measured
How we scored 136 real support conversations and 533 assistant actions step by step, with no labeled examples to check against — and what it revealed about quality, cost, and speed.
Original productionReference · earlier software version
$23.31
21 s
Smart routerFewest made-up facts and wasted actions
$4.34
11 s
Luna · budgetWeakest quality, ~9× cheaper
$0.48
8.7 s
Terra · fastFastest, least wasteful fixed model
$4.44
8.3 s
No single winner. The trade-offs between quality, cost, and speed are real — and now quantified.
A named defect. The judges caught replies addressed to the wrong person — an engineering fix, not a research problem.
~60 actions where judges disagreed, handed to human reviewers instead of averaged away.
Have an agent that needs evaluating?
Tell us what it does and what decision you're facing — or send a batch of production traces. We'll reply within two business days with how we would approach it.
Get in touch