Test what an AI agent
will do before customers find out.
Golden datasets, tool-call validation, adversarial testing, and regression gates — wired into your release process, not a one-time report that goes stale in a month.
Find out what it will do
before customers do.
Most agent failures aren't visible until a customer hits the exact edge case nobody tested. We build the evaluation layer that catches it first — golden datasets, tool-call validation, adversarial testing, and regression gates wired into your release process.
This is the natural complement to AI agent development and LLMOps/AgentOps — build, evaluate, and operate as one discipline, not three disconnected steps. It matters even more for multi-agent systems, where a failure can originate in one agent and only surface as a wrong outcome several steps later in a different one.
What we actually
test for.
What you
actually get.
An eval plan, a test set built from real and representative inputs, a working harness, a baseline score, agreed release thresholds, a monitoring dashboard, and a remediation backlog ranked by severity — not a slide deck summarizing findings you can't act on.
| Eval-set category | Example composition | What it catches |
|---|---|---|
| Happy-path tasks | ~40% of set — the core workflows the agent is built for | Baseline regression on the common case |
| Edge cases | ~25% — ambiguous phrasing, incomplete inputs, unusual but real requests | Graceful degradation instead of a confident wrong answer |
| Adversarial / out-of-scope | ~20% — deliberate attempts to push the agent outside its permissions | Scope violations and prompt-injection-style attacks |
| Regression cases | ~15% — past failures, kept permanently in the set | A fixed bug quietly coming back on a later model or prompt change |
Release thresholds are agreed per category before launch, not set generically — a typical structure requires ≥98% pass on happy-path tasks, ≥90% on edge cases, zero tolerance on adversarial/scope-violation tests, and no regression-case failures, with any miss blocking the release rather than shipping with a known issue and a follow-up ticket.
What the dashboard
actually shows.
Automated scoring catches most cases; a sample of live traffic still gets human review, because some failures only a person recognizes as wrong.
A defined percentage of live traffic (higher for new releases, lower once stable) is scored by a reviewer against a rubric, not just a pass/fail model judge.
The dashboard shows pass rate per eval category over time, so a regression shows up as a trend line, not a single bad report after the fact.
Every failure is logged with severity and frequency, feeding a backlog your team can actually triage instead of a wall of raw transcripts.
Audit once,
or operate continuously.
A point-in-time evaluation of an existing agent, with a written report and severity-ranked findings.
The harness runs on every release, with monitoring and a dashboard your team owns.
Evaluation designed in from day one on a new agent build, not retrofitted after launch.
Guardrails that
caught the problem first.
Our agent guardrails case study describes the class of failure this service exists to prevent: an agent taking the wrong action silently, caught only after the fact. Schema enforcement, whitelists, and human-in-the-loop gates turned that into a rejected call before execution instead of a cleanup afterward.
No sales pitch,
just how we work.
Evaluation design comes from people who've shipped the thing being evaluated.
You get a prioritized backlog, not a wall of observations.
Regression testing runs where your team already ships code.
You own the harness and dashboard at completion, not a locked black box.
Tools we've shipped
in real projects.
Before you
book a call.
Can you test an agent we did not build?
Yes — a fixed-scope audit of an existing agent is one of our two engagement models. We review the architecture, build a golden dataset, and hand you a report with concrete findings and severity ratings.
What is a golden dataset and why do we need one?
A set of representative inputs with known-correct outputs, used to score every change before release. Without one, "did this change make the agent better or worse" is a guess instead of a measurement.
How do you test tool calls specifically?
We validate the arguments an agent passes to a tool against a schema, check permission boundaries are respected, and run adversarial inputs designed to push the agent outside its scope.
Does this replace our existing QA process?
No — it extends it. Standard QA tests deterministic code paths; agent evaluation tests probabilistic behavior across a distribution of inputs.
What do we get at the end of the engagement?
An eval plan, a test set, a working harness, a baseline score, agreed release thresholds, a monitoring dashboard, and a remediation backlog ranked by severity.
How do you structure a release threshold so it actually blocks a bad release?
Per-category pass rates agreed before launch — typically near-100% on core happy-path tasks, a lower but still high bar on edge cases, and zero tolerance on adversarial or scope-violation tests. Any miss blocks the release; it's a gate, not a report that gets read after the fact.
Is testing fully automated, or does a person review anything?
Both. Automated scoring against the golden dataset runs on every change; a sampled percentage of live traffic also gets human review against a rubric, because some failure modes only a person reliably recognizes as wrong.
Tell us what
the agent is allowed to do.
Book a 30-minute call — we'll scope an audit or an ongoing evaluation setup based on what's actually at stake.