TL;DR: We build production AI agents the same way we test them — map the workflow first, wire every tool behind a typed guardrail, and build a golden set of real cases before tuning anything. Then we gate each change with evals in CI, so a prompt tweak, model swap or new tool can't quietly make the agent worse. The result is an agent you can change without fear, because a regression is caught before it ships, not after your users find it.
An AI agent that demos well is easy. An agent that stays reliable in front of real users, real data and real money is the hard part — and it's where most projects quietly fall apart.
We build agents for a living, and we came from QA before we wrote a line of product. So we build the agent and the thing that proves it works in the same motion. Here's how.
Start from the workflow, not the prompt
A single clever prompt is a demo, not a system. Real work has steps, branches and failure modes. We start by mapping the workflow the agent is meant to own: what it decides, which tools it calls, what it must never do.
That map becomes the architecture — usually a planner that breaks work into steps and an executor that carries them out, recovering when a step fails instead of collapsing into a wrong answer.
Wire tools with guardrails, not hope
The moment an agent can call tools, it can do damage. So every tool gets a typed interface and a guardrail: what arguments are valid, what actions need confirmation, what it can never touch. An agent that can move money or delete data is only as safe as the checks around those calls.
Build the golden set before you tune anything
Here's the part teams skip. Before tuning prompts or swapping models, we build a golden set — representative inputs drawn from real usage, each with an expected behaviour. It's the ground truth every future change is measured against.
Without it, "is this better?" is a matter of opinion. With it, it's a number.
Gate every change with evals in CI
The golden set only pays off when it runs automatically. We wire evals into CI so that every prompt tweak, model upgrade or pipeline change is scored before it can ship:
- Behaviour and hallucination — is the answer correct and grounded, or invented?
- Tool-call safety — did it call the right tool with the right arguments, and refuse the unsafe ones?
- Regression — did this change quietly break something that used to work?
If a change makes a key metric worse, the merge is blocked. That's the whole point: the agent can't get worse without someone deciding to let it.
Then we try to break it
Building and grading isn't enough for anything adversarial. Before an agent goes live, we attack it the way a real adversary would — prompt injection through user input and through the documents it retrieves, jailbreaks against its guardrails, attempts to make it misuse a tool or leak data. What we find becomes a finding with a repro and a fix, and then a regression test so the hole stays closed.
The result
An agent you can change without fear. New prompt, new model, new tool — the evals tell you within minutes whether it got better or worse, and the gate stops a regression before your users find it.
That's the difference between an agent that demos great and one that ships. We build both halves, because one without the other doesn't survive production.
Building an agent, or have one that keeps surprising you in the wrong ways? Tell us what you're building — we'll scope it.