Capability
Evaluation design
Turn business expectations and failure risks into test cases, scoring criteria, and release thresholds.
AI Engineering / Agent & Evaluation Harnesses
Know how your AI behaves before users have to tell you.
Make model and agent changes measurable with representative cases, release thresholds, and observable production runs.
Discuss this serviceThe business problem
What brings this work into focus.
AI quality can shift when prompts, data, models, or connected tools change. Without representative tests and observable runs, teams cannot compare versions, investigate failures, or decide whether a release is ready.
System delivery
Define the boundary, build the capability, and prepare it for real use.
Define the system
Build in working slices
Measure and operate
Capability
Automated test harnesses
Run repeatable checks across model responses, retrieval results, tool calls, and full agent trajectories.
Capability
Production observation
Capture useful traces and failure categories so the team can diagnose change without unnecessary sensitive content.
Working outputs
What the engagement produces.
- Versioned evaluation dataset and rubric
- Automated evaluation pipeline with baseline results
- Monitoring and release-review playbook
Fit guidance
Use the approach that matches the constraint.
This is useful when
Needed when an AI feature is moving toward production, changes frequently, or performs work where inconsistency carries a meaningful cost.
A simpler path may be better when
Use a compact manual review set for an early experiment with low volume and no operational dependency.
Related offerings
The adjacent work that often matters.
Start a conversation
Talk with us about agent & evaluation harnesses.
We will help you identify the useful first move and say plainly when a simpler option is the better answer.