An agent is software that works towards a goal on its own: it reads a request, decides what to do, uses your tools to do it and reports back. The useful ones do one job well. We build agents around a single workflow at a time, such as qualifying inbound leads, triaging support tickets or preparing research briefs, and connect them to the systems your business and team already uses, with clear limits on what they may do without asking.

Our guide AI agents in production covers what it takes to run one safely.

Most agent projects are one of four kinds.

Every agent runs the same loop: it reads the request, plans, calls a tool, reads the result and decides the next step, until the job is done or it needs a person. The scene follows one inbound lead through that loop: a look-up of the company, a check of the CRM, a search of the documentation, then a reply that answers both questions and offers a call.

Each step is logged with its inputs, its output and how long it took, so any decision the agent made can be traced afterwards.

Models don't know your prices, products or policies, and what they learned in training goes out of date. So the agent looks things up when it needs them. Your documents are split into passages and indexed by meaning, the question is matched against them, and the best few passages go into the model's context.

The reply cites the passages it used, so your team can check where each claim came from. When nothing relevant turns up, the agent says so instead of guessing.

An agent acts through tools: functions with a name, a description and typed inputs that call your CRM, your calendar or your email. Each tool gets only the access its job needs, read-only where reading is enough and one named channel rather than the whole workspace. A call outside that list is refused, not attempted.

Where it fits, we connect tools through the Model Context Protocol (MCP), an open standard now governed under the Linux Foundation, so the same integration works across models and clients. Tool descriptions from third-party servers are treated as untrusted input, and every connection gets the narrowest scopes it needs.

Some actions should wait for a person: a discount above the agent's limit, a refund, a first email to a new contact. The agent prepares the action with its reasons and asks where your team already works, such as Slack, and nothing happens until someone approves it. As the agent proves itself in review and on the test set, its limits can rise step by step.

Before an agent ships, and after every change to its prompt, model or tools, it runs a test set of real cases with known good outcomes. Each answer is scored by code where it can be checked exactly, by a grading model where it can't, and by people for a sample. A change ships only if it clears the gate.

The chart shows one test set run across many configurations: most fall short, a few pass, and the best one ships.

Start with one workflow and a measured baseline, prototype on real cases, evaluate before go-live, then operate with review and monitoring. The agent's autonomy grows as its scores hold.

A production agent, its integrations and knowledge sources, an evaluation suite, a review console for your team, and monitoring with runbooks. You own the prompts, the code and the data.

Models from several providers, chosen per task on the evaluation scores; retrieval over your documents and data; typed tools, connected through MCP where it fits; tracing, evaluation runs and cost monitoring in production.

Answers to the questions we hear most often about agent projects.