An AI agent is software that takes a process from start to finish instead of waiting for clicks: reading the request, applying the rules, doing the work, and escalating what it should not decide alone. We build them where a task is repetitive, rule-heavy, and measured in hours rather than minutes.
Strip the marketing and an agent is a program that decides its next step from context instead of from a fixed script, and that can call the tools it needs to finish the job. That flexibility is exactly what makes it useful on messy real-world input, and exactly what makes it dangerous without limits. Both halves are engineering problems.
Quoting, onboarding, claim triage, document extraction, back-office reconciliation — work where the rules exist and are written down somewhere, the input is unstructured, and a person is currently spending hours doing what amounts to careful reading. If a task is already a clean form with clean data, you want ordinary software, and we will tell you so.
Every agent we ship comes with an evaluation set: real cases, expected outcomes, and a score that runs on every change. Without that, a change that improves one case quietly breaks four others and nobody notices until a customer does. The evaluations are yours at handover, because they are what make the thing maintainable.
We draw the line at cost of being wrong. Reading a hundred invoices and proposing the ledger entries: agent. Sending money: person. Drafting the reply to a complaint: agent. Deciding the refund: person, until the numbers say otherwise. Designing that boundary is most of the work, and it belongs in the first conversation.
Decisions that need accountability nobody is willing to sign for. Processes whose rules exist only in one person's head — write them down first, and often you discover you did not need an agent. And anything where the failure mode is a person being treated unfairly without recourse.
Count the hours the process takes today and how much of it is judgment versus reading. If most of it is reading and applying rules that exist in writing, an agent pays for itself quickly. If most of it is judgment, automation makes it worse, faster.
Whichever fits the task, the latency budget and the privacy constraints — we are not tied to one provider, and the architecture keeps the model swappable. That matters more than the choice itself: the frontier moves every few months and you should be able to move with it.
We design for the smallest amount of data that does the job, and we can keep processing inside infrastructure you control when your requirements call for it. If you need a data-processing agreement or EU-hosted infrastructure, that is a constraint we build to, not an obstacle.
Thirty minutes. We will tell you honestly whether it is an agent, ordinary software, or a rulebook nobody wrote down yet.