Know exactly where agents fail — and what to fix.
Not a pass/fail checkbox. A multi-dimensional profile of agent behavior against your operational requirements.
A benchmark is a full simulation: tasks, tools, data sources, and constraints that mirror your production environment. Your agent executes the workflow end to end — the same way it would in the real world.
An evaluator measures one dimension of agent output — grounding, policy compliance, tool usage, tone. Chain multiple evaluators together, weight them for your domain, and get a composite score that reflects your priorities.
Benchmarks simulate. Evaluators score. Together they tell you exactly what your agent can do.
For development teams.
- → Pinpoint failures by dimension
Safety at 0.94 but process compliance at 0.55? You know exactly what to fix without guessing.
- → Regression detection before deployment
Every change is scored. If a prompt edit hurts safety, you see it immediately — not from user complaints.
- → Composable chain — add domain-specific checks
Chain statistical checks, rule validators, and LLM judges in any order. Weight each for your domain.
Four evaluator families. Each covers a blind spot the others can't.
Different domains demand different quality definitions. A clinical agent needs safety at 0.30 weight. A logistics agent needs route accuracy. One fixed scoring system can't serve both.
Latency distributions, token usage, cost per run, completion rates
Format compliance, required fields, escalation triggers, safety guardrails
Tone, factual accuracy, hallucination detection, reasoning quality
Entity resolution, citation verification, financial impact, signal detection
Three aggregation methods — because risk models differ.
Weighted means hide safety failures behind high averages. Minimum aggregation catches them but penalizes breadth. The right method depends on your risk model.
Each evaluator contributes proportionally to what matters most for your use case.
One failed dimension fails the whole chain. No averaging away safety violations.
Category minimums with weighted rollup. The strictest option for regulated domains.
Per-task overrides — because agents serve multiple stakeholders.
A single agent may handle compliance reviews and customer queries. These aren't the same job. Override weights merge with the base chain so each task carries its own definition of 'good enough.'
For the business.
Score accuracy and tone before deployment. Catch agents that will generate escalations — before they do.
Every agent version scored against your policy requirements. Audit trail for regulators. Certifiable.
Compare 3 LLM providers on the same benchmark. Data-driven decision instead of vibes.
See how your agent actually performs.
Your data, your dimensions, your scoring rules.