Ready to Ship Faster with Confidence?
From golden datasets to regression tested evaluation gates let's make AI quality measurable and continuously improve your production systems.
AI Evaluation
Evaluation Measure Quality Before It Ships
Treat agent quality the way classical ML treats accuracy: a measurable, regression-tested release gate. Every prompt, every tool call, every response gets scored against curated datasets so your team ships with confidence not guesswork. Evaluation catches regressions before they reach users and turns AI quality from a gut feeling into a repeatable process.
Explore Our Evaluation Approach
Quality at Speed
1.2% confabulation rate
Was 3.8% before evaluation gates
Measurable Quality
Evaluate agent quality against curated datasets, expected tool sequences and defined evaluation criteria.
Regression-Tested Releases
Measure changes continuously and use evaluation results as a release gate.
Reproducible Reality
Record real LLM and MCP server interactions, then replay them deterministically.
Explicit Judgment
Use explicit rubrics and LLM as judge evaluation to assess quality where exact output matching is meaningless.
Evaluation Strategy
Evaluation Builds Trust Continuously.
Trust your team's changes. Trust the model in production. Four moves: what to measure, how to measure, how to iterate, and what to evaluate for.
The RAG Triad
Answer relevance, context relevance and groundedness measured, not guessed.
Tiered Eval Stack
Cheap automated signals plus verified human ground truth at every uncertainty level.
Iterate Like TDD
Every feedback becomes new eval data. Feedback → Iterate → Evaluate → Deploy.
Evaluate for RHH
Honest, Harmless, Helpful scored on explicit rubrics, not vibes.
Testing Pyramid
The Agent Testing Pyramid
Layers map to uncertainty tolerance, not test types. Agents break the same input → same output assumption immediately.
Unit tests with mocked LLM providers. Retry behavior, max turn limits, tool schema extension management, and subagent delegation. Did we write correct software?
Record real LLM and MCP server interactions once, then replay them deterministically forever. Assert tool call sequences and flow - not exact outputs.
Structured benchmarks run many times. Regression means success rates dropped - not simply that the output changed. Statistically meaningful at scale.
LLM as judge with explicit rubrics. Each evaluation runs multiple times and uses majority judgment to smooth noise. Human intuition meets machine consistency.
Our Process
A Simple 5-Step AI Evaluation Roadmap
From curated evaluation data to a regression-tested release gate a continuous path to reliable AI systems.
01
Define
Build the Golden Dataset. Curate prompts and expected tool sequences that represent the evaluation use case.
02
Run
Evaluate the Candidate Agent. Run the candidate agent against the curated evaluation set.
03
Judge
Apply LLM as Judge. Evaluate tool correctness, call order and answer relevance using explicit criteria.
04
Measure
Track Evaluation Signals. Monitor relevance, groundedness, tool-selection accuracy and other quality signals.
05
Gate
Pass / Fail the Release. Use evaluation results as a regression tested release gate before changes ship.
Pipeline
From golden dataset to release gate
Golden Dataset
300+ curated prompts & expected tool sequences.
Candidate Agent
PR build runs full evaluation in CI.
LLM-as-Judge
Tool correctness, order accuracy, answer relevance.
Pass / Fail
PR merge gate auto-tagged in Arize.
Evaluation Metrics
Quality You Can Measure
Live signals from the evaluation pipeline before changes ever ship.
Confabulation Rate
1.2%
was 3.8%
Tool-Selection Accuracy
96.4%
Mean Answer Relevance
4.6 / 5
Scored on three dimensions
- 1 Did the agent pick the right tools?
- 2 Did it call them in the right order?
- 3 Was the answer grounded in tool output?