Agent evaluation, observability, and safety rails
Test sets, scoring, tracing, cost monitoring, and the input and output controls that make an AI system measurable rather than merely impressive in a demo.
Agent evaluation means scoring a system against a set of cases with known correct answers, so quality is a number rather than an impression. Guardrails are the input validation, output checks, permission scoping, and spend limits that contain failures when they happen. It is for anyone about to put an AI system in front of customers, and for teams who already have one and cannot say whether it is getting better or worse.
The problem
AI systems demo well and degrade invisibly. A prompt change that fixes one case breaks three others, and without a test set nobody finds out until a customer does.
- Quality is assessed by whoever built it trying a few examples they remember.
- A model version or prompt change ships with no way to tell whether it helped.
- Nobody knows the error rate, so nobody can decide if it is acceptable.
- When output is wrong, there is no trace showing what the model was given.
- The monthly bill is a single number with no attribution to any workflow.
What we build
An evaluation set from your data
Real historical cases with known correct answers, including the awkward ones. Built from your work, because generic benchmarks say nothing about your workflow.
Scoring that matches the task
Exact match for extraction, rubric or model-graded scoring for drafted text, task completion for agents. Chosen per workflow rather than applied uniformly.
Regression testing in CI
The suite runs on prompt and model changes, so a change that improves one case and breaks four is caught before it ships.
Tracing
Every run recorded: inputs, tool calls and their arguments, model outputs, latency, and token cost. This is the difference between debugging and guessing.
Input and output guardrails
Validation before the model sees input, schema and policy checks before output is used, and refusal paths for requests the system should not handle.
Spend and rate limits
Per-run and per-day ceilings with alerts, so a loop or a traffic spike is a capped incident rather than an unexpected invoice.
What you get at handoff
- The evaluation set and scoring scripts in your repository.
- CI configuration running the suite on every relevant change.
- Dashboards for accuracy, latency, cost, and error rate that you own.
- A documented baseline, so future changes are measured against something.
- A written policy on the error rate the workflow can tolerate, agreed rather than assumed.
Stack
Evaluation
Fixture-based test sets, Model-graded scoring, Rubrics, pytest
Tracing
Structured run logs, OpenTelemetry, LLM observability platforms
Guardrails
Schema validation, Structured outputs, Allow and deny lists, Refusal paths
Cost control
Token accounting, Per-run budgets, Rate limiting, Spend alerts
CI
GitHub Actions, Regression gates on prompt changes
Related services
FAQ
How many test cases do we need?
Fewer than people expect to get value, and they need to be the right ones. Thirty to fifty real cases covering the common paths and the known-awkward ones will catch most regressions. The awkward cases matter more than the volume.
How do you score something subjective, like a drafted email?
With a rubric applied consistently, often by a model scoring against explicit criteria, spot-checked by a person. It is less precise than exact-match scoring and still far better than no measurement, because it makes a regression visible.
What error rate is acceptable?
That is a business decision, not a technical one, and it should be made explicitly. Misrouting an internal ticket is cheap. Sending a customer a wrong price is not. We push for that number to be agreed and written down, because it determines how much human review the workflow needs.
Can you add this to a system we already have?
Yes, and it is a common engagement. Adding tracing and an evaluation suite to an existing agent usually reveals a real error rate that nobody had measured, which is uncomfortable and useful.
Is this not overkill for a small business?
The scaled-down version is not: a test set, a trace log, and a spend cap. That is a few days of work and it is what separates a system you can improve from one you can only hope about.
Ready to scope Evaluation and Guardrails?
Send the workflow, tool stack, or reporting problem. We will tell you what should be automated, what should stay manual, and what is worth building first.