Score-only pipelines
A failed grade without an approved replacement does not change behavior.
LLM evaluation
Find out where your AI fails and test the fix.
We review real conversations and tool calls, agree what counts as a good result, and compare your current agent with the proposed change. You get scored examples, approved corrections, and a recommendation on what to deploy.
A score doesn't tell you how to fix the problem.
Review the failed reply or action, write the correction, and test whether the change works on other cases.
Conflicting instructions
Adding a rule for one failure can introduce problems elsewhere. Test the other tasks too.
Offline benches only
Lab sets miss production tools, policies, and messy user language.
Lost corrections
Edits left in tickets can't help future tests unless someone records and reviews them.
Test the change before committing to it
We use reviewed examples to test a prompt change, retrieval, or fine-tuning. Then we compare the results with your current setup.
Tests from real tasks
Use your conversation logs and tool results to decide what the agent needs to get right.
Approved corrections
Your reviewers specify what the agent should have said or done.
Before-and-after results
Compare completed tasks, human edits, handoffs, response time, and cost for one task.
What an evaluation service should cover
Agree what counts as success, save repeatable test cases, and check for new failures whenever the prompt, model, or tools change.
Evaluation design
Define task-level success criteria, representative scenarios, and the mix of automated graders and human review needed for the workflow.
Regression workflow
Version scenarios, prompt/tool changes, and expected outputs so a release can be compared to a stable baseline.
Production feedback loop
Bring real failures back into the set, then decide whether a correction belongs in retrieval, a prompt, a tool policy, or a future training run.
Build the approved examples used in evaluation with golden datasets for AI agentsApply the same workflow to call outcomes through voice AI testing services
What the evaluation includes
Use the results to decide what to change and whether it is ready to deploy.
Evaluation work
Write scoring rules, review real logs, approve corrections, and compare results before and after the change for one task.
Automated and human review
Automate repeatable checks. Use people for ambiguous cases and decisions that need policy or tool expertise.
Supported inputs
OpenTelemetry exports, conversation logs, ticket threads, and tool-call traces with version metadata.
Sample report outline
Failure clusters, rubric scores, corrected examples, baseline comparison, and a rollout recommendation.
LLM vs agent evaluation
LLM evaluation can focus on responses; agent evaluation must also score tools, state, recovery, and escalation.
Files you receive
Versioned scoring rules, reviewed examples, approved corrections, and a deployment recommendation for the task tested.
Free template
Download the AI agent evaluation rubric
A CSV scoring template for task outcome, tools, state, recovery, escalation, safety, cost, and latency.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
Who this is for
- Teams shipping AI agents who need reliable, repeatable evaluation
- Support, voice, and ops workflows where mistakes are expensive
- Leaders comparing fine-tuning vs retrieval of approved examples
- Teams that need reviewed corrections alongside test scores
What the sprint includes
Review
Identify recurring mistakes and agree what a successful result looks like.
Build
Prepare approved corrections for the agreed retrieval or training test. Keep test cases separate.
Evaluate
Compare replies without showing reviewers which setup produced them, then recommend whether to deploy or keep testing.
Measure the result
We test one task before recommending a wider rollout. We can't promise improvement before reviewing your data.
Limitations
- A high score on an offline bench does not prove production readiness.
- Score-only reports without approved replacements rarely change agent behavior.
- Allow time for reviews and agree who resolves disagreements.
- Before/after comparisons are only interpretable when the workflow and baseline stay fixed.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
Common questions
What does the work include?
We use reviewed examples to test a prompt change, retrieval, or fine-tuning. Then we compare the results with your current setup.
Do you fine-tune our model?
If it's worth testing after we review the problem. We try your best prompt and consider better examples or tool fixes first. We test an adapter on cases it wasn't trained on and check for new failures. The contract sets delivery and usage rights; the base model's license still applies.
What do we need to start?
Logs or conversation exports for one task, examples of good results, and a reviewer who can approve corrections. We agree data handling before you send anything. Remove unnecessary personal details.
How is success measured?
We test the existing and changed agent on the same task. Where the data is available, we compare completed tasks, human edits, handoffs, response time, and cost.
Related
Discuss what you need to test.
Bring examples of mistakes your agent makes. We'll discuss the tests, reviewers, and work needed to assess a fix.