How is Reliability & Evaluation different from AI Operations?

Operations watches how the system behaves once it's live. Reliability & Evaluation defines what "good" looks like ahead of time, builds the test sets, runs the checks, and blocks a bad change before it ever ships.

How long does it take to build a testing system?

It depends on how many behaviors need covering. The first version comes together in four steps: defining the rules, building the test sets, tuning the scoring, and wiring it into your release pipeline.

Do we need to use a specific testing tool?

No. We work with whatever fits your stack — tools like DeepEval, Ragas, promptfoo, or OpenAI Evals, or something custom-built. The choice depends on your setup and use cases.

What if our production data is sensitive?

We build the test sets from your production activity with anything personally identifying removed. The data stays inside your own infrastructure — none of it leaves your environment.

How often do the pass/fail thresholds need to be updated?

Every quarter is standard. As the system's behavior and use cases change over time, we monitor the thresholds and recommend updates.

Can this catch every failure?

No. Coverage improves over time — the first version catches the obvious regressions, and later updates catch more edge cases. Some failures won't show up until production; the goal is to catch most of them early and learn fast from the rest.

Prove the system didn't get worse this release.

AI systems don't behave the same way twice. Every instruction change, model update, or data shift can quietly make things worse. Before you ship, you need proof: a saved set of test cases, a scoring system tuned to your business, and a check that blocks the release when quality drops.

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Problems caught before they ship, not after.

The same tracking, alerting, and monitoring tools your engineering team already runs — wired up to catch quality drops, not just outages.

PrometheusGrafanaOpenTelemetryJaegerPagerDutySentryPrometheusGrafanaOpenTelemetryJaegerPagerDutySentry

Recognize the symptoms

The symptoms that show up first.

If two or more of these are already true, the next release is a bet, not a decision.

A prompt change that improved one use case silently broke another
A new model scored higher on public benchmarks but got worse in production
The same question gets a different answer depending on when you ask it
Nobody can say whether this release regressed until a customer notices

Normal software testing assumes the same input always gives the same output. AI doesn't work that way.

A change that improves one use case can quietly break another, and a model that scores higher on public tests can still perform worse on your real work.
Updating the data a system searches over can cause it to make things up, and small rule changes to an agent can break how it calls its tools.
Without structured testing, teams ship changes they can't be sure are safe; without a release check, those failures reach production without anyone noticing.
We build testing into the workflow from the start: a saved test set, a scoring layer, repeated measurement, and a release check that catches problems before they ship.

Why AI reliability is different

It's not predictable — the same question can get a different answer each time you ask it

A model update that looks better on public tests can still perform worse on your actual work

A small wording change to an instruction can shift behavior a lot, including new refusals

A change to one tool, model, or rule can ripple through the whole system unexpectedly

A change to the underlying data or search index can alter answers without anyone noticing

A complete testing system, not just one test suite.

Five pieces, built and run together.

Rules for what "good" means

For each workflow, we write down what a correct answer looks like, what's never allowed, and how bad a failure has to be before it blocks a release.

Test sets

A saved, version-tracked set of real questions and edge cases pulled from production and public benchmarks, scored by a grader tuned to your business.

Automatic checks before every release

Every code change runs against the test set automatically. If quality drops, the change gets blocked until it's fixed or someone signs off on the exception.

Live dashboards

Production activity feeds back into the same scoring system, so a drop in quality shows up right away instead of weeks later.

Learning from real failures

When something breaks in production, we trace it back to the test set and update the tests and thresholds so the same failure gets caught next time.

From our experts.

Writing on agent failure modes, benchmarks, and production observability.

Production & Engineering

AI Agent Failure Modes in Production: The Four That Actually Kill Systems

The four failure modes that repeat across production deployments: tool-calling errors with cascading impact, infinite retry loops burning budgets, hallucination cascades in multi-agent systems, and silent quality drift.

14 min read

Production & Engineering

AI Agent Observability: What to Track in Production

The three critical observables for production agents — tool-call trajectory, memory operations, workflow visibility — plus implementation patterns and a Langfuse vs Braintrust vs Honeycomb comparison.

14 min read

Production & Engineering

Why "Matches Frontier" Benchmark Headlines Mislead Buyers

A benchmark win is not a procurement decision. Agentic coding scores move with benchmark version, model date, harness, resource budget, and task selection.

10 min read

The same nine-phase process, applied to reliability.

From discovery to handoff, with a real check at every step instead of just hoping the release goes fine.

DiscoverArchitectPlanBuildValidateDeployOperateOptimizeTransfer

Common questions.

Build the testing system that catches failures before they ship.

Tell us your workflows, data constraints, and risk tolerance. We'll design a testing system built around your needs, then train your team to run it.

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