Releases rely on spot checks
Someone tries a few prompts before each release. Nobody can say what changed, what regressed or how often the agent actually succeeds.
SECONDARY SERVICE
Make evaluation part of how your team ships.
We implement reusable evaluation workflows in your existing tools: versioned datasets, code checks, calibrated judges, human review and release gates, with documentation and a clear handover.
Signs you need this
You bought a tool but not a practice
An evaluation platform is installed, but datasets are thin, judges are uncalibrated and the scores don't drive decisions.
Model changes feel risky
New model versions arrive often. Without a repeatable comparison, every upgrade is either delayed or taken on faith.
Build the evaluation foundation
Dataset creation
Convert approved interactions, business scenarios and expert examples into versioned tests. Tag by workflow, difficulty, risk and expected outcome. Review synthetic cases before admitting them.
Code-based checks
Schema validation, business-rule assertions, tool-call and argument checks, state verification, executable tests and latency or cost budgets where they apply.
Judge design and calibration
Dimension-specific rubrics, the right context, evidence requirements and comparison with expert labels. We test order effects and recalibrate when models or prompts change.
Outcome verification
Checks that inspect the systems the agent changed, so a confident final message cannot hide a missing or wrong action.
Simulation and fault scenarios
Simulated users for multi-turn journeys, and controlled tool failures such as timeouts, malformed responses and unavailable services.
Repeated-run reporting
Run selected cases multiple times and report consistency, so the team sees reliability rather than a single favorable result.
Make it repeatable
Human review workflow
Who reviews ambiguous cases, how reviewers apply the rubric, and how disagreements are resolved. Calibration examples stay separate from final evaluation cases.
Release integration
Agreed suites run on relevant changes. Critical checks act as gates; probabilistic scores are read with sample size, variability and error bars.
Ownership and handover
Documented configuration, datasets, run commands, scoring rules, failure triage and maintenance responsibilities. We train your team to add and review new cases.
Works with your tools
Your evaluation platform
We implement in Braintrust, Confident AI and DeepEval, Arize Phoenix, Promptfoo, Giskard, Patronus AI or a lightweight custom harness.
Explore serviceYour cloud
We work with native evaluation features on AWS, Azure and Google Cloud where they fit, alongside your CI/CD pipeline.
Explore serviceYour models
Agents on Anthropic Claude, OpenAI or other models. Configuration is recorded so comparisons remain fair across versions.
Explore serviceImplementation deliverables
Working evaluation harness
A runnable workflow tied to the agreed agent interface, model configuration and test environment.
Versioned assets
Reviewed datasets, evaluator code, rubric prompts, thresholds and baseline results under version control.
Operating playbook
How to run comparisons, review failures, promote new tests and update the evaluation as the agent changes.
Engagement stages
Design
Agree workflows, metrics, gates, tooling and responsibilities. Review existing tests and data sources.
Build and calibrate
Create datasets and evaluators, calibrate judges against expert labels and connect suites to your release process.
Transfer
Pair with your engineers on real changes, document everything and confirm the team can run the process without us.
Make your next decision with evidence.
Start with one workflow, clear success criteria, and an evaluation plan your team can use.