Your pilot works. Can you prove it?
Agents fail quietly. A prompt gets edited, a model version moves, a tool's schema shifts — and quality drops without a single error being raised. We build the harnesses that measure what an agent actually does, so the decision to run it unattended rests on evidence instead of a good demo.
This is where most projects stall
- +40%
quality gain on tasks inside AI's capability — and measurably worse output on tasks outside it. Same tool, same people.
Source: Dell'Acqua et al. 2023, Harvard Business School / BCG, pre-registered field experiment with 758 consultants; published in Organization Science, 2025
- 46%
of AI projects die somewhere between proof-of-concept and broad adoption
Source: S&P Global Market Intelligence 2025, Voice of the Enterprise: AI & Machine Learning, 1,006 professionals in North America and Europe
- 39%
of organisations can attribute any enterprise-level EBIT impact to their AI work
Source: McKinsey QuantumBlack, The State of AI in 2025, 1,993 respondents across 105 countries
Scoring the final answer tells you almost nothing
An agent can produce a fluent, plausible reply having called the wrong tool, skipped a policy check and never completed the work. These are the four behaviours that decide whether it is safe to run unattended.
01 · Tool selection
Did it reach for the right thing
The right tool, in the right order, with arguments that validate against the schema. This is deterministic and unarguable — assertions, not opinions — and it catches most real regressions before anything subjective is involved.
02 · Task completion
Did the work actually happen
An agent that writes a confident summary of a refund it never issued has failed, however well the summary reads. Completion is checked against the state of the systems the run was supposed to change.
03 · Policy compliance
Did it stay inside the boundary
Thresholds, escalation rules, approval limits, data it should never have touched. An agent that reaches the right answer by stepping around an approval gate has failed the test, not passed it.
04 · Failure behaviour
What it does when things break
A timeout, a malformed response, an input too ambiguous to act on. Graceful failure is a testable property, and it is precisely the property a demo is built to avoid ever showing you.
A run you can read, not a score you have to trust
Every case carries its own verdict and the reason behind it. Crucially, a deterministic assertion failure and a judge scoring lower are shown as different kinds of finding — because one is a build gate and the other is a question for a person.
Three jobs, and only one of them happens before launch
Pin the behaviour
Hand-labelled golden cases hold the behaviour still, so a prompt edit or a model upgrade has to prove it did no harm. New providers and new prompts run against the current baseline before they go anywhere near production.
Score the open-ended
Where the output is prose there is nothing to assert against, so rubric-driven judges give a score that is at least comparable between runs. Retrieval gets measured properly — precision, recall, faithfulness — instead of being judged by how the answer reads.
Watch it in production
No offline suite sees real traffic. Traced runs, sampled human review and drift signals tell you when live behaviour has wandered away from the cases you wrote, which is usually where the interesting failures were hiding.
The same suite, three models, one decision
Model choice is an evidence problem, not a preference. Running one suite across candidates turns it into a comparison you can defend to whoever signs the invoice — and shows you what you are trading away when you pick the cheaper one.
Automated scoring is diagnostic. Human review is authoritative.
A judge is a model with an opinion, and a rubric encodes the assumptions of whoever wrote it. A green suite is evidence, not proof. So we build harnesses that report which cases changed and why, keep deterministic assertion failures separate from a judge's disagreement, and put the cases that genuinely need a person in front of a person. We would rather show you a disagreement than average it away.
Questions engineers ask
Bring us the agent you cannot sign off on
We will tell you what it does, where it breaks, and what it would take to trust it running unattended.