Custom AI agents and agent teams
An AI agent does one defined job the way a trained new hire would: it gathers what it needs, does the work, and hands you the result to check. When a job is bigger, we build a team of agents that split it up and pass the work along, with a person reviewing what matters.
Who it’s for
The work happens every day or week, follows a pattern, and someone can describe what good looks like.
AI can do most of it, and a person should approve the parts that matter.
Research, then drafting, then checking. When each step needs its own skills, an agent team fits.
You work in Claude and want agents built with Claude Code, skills, and connectors.
Agent teams in plain English
Each agent has one job and the instructions for it. Work passes from one to the next, and a person checks it before anything leaves. The number of agents matters less than the work handled and what you get at the end.
The schedule starts a run with today’s list of prospects.
Gathers public information on each company and its recent news.
Drafts a one-page brief and an opening email in your format.
Compares each draft with your rules and flags anything uncertain.
Approve, edit, or reject each email before it’s sent.
Saved to your CRM before the first call of the day.
One agent or a team?
Most work needs one well-built agent. A team earns its extra upkeep when the job has distinct steps that need different instructions.
What you get
Each agent’s job written down: inputs, steps, the result, and what good looks like.
The instructions, templates, and reference material each agent works from, built from your own examples.
For agent teams: who does what, in what order, and what gets passed along at each step.
The places where a person approves, edits, or rejects the work, set where the risk is.
Runs on real examples, compared with what your team would produce, before anything goes live.
Running where your team works, with plain-language docs on how it works and how to change it.
Examples
Illustrative scenarios that show the kind of work involved. They are not client results.
How it works
Inputs, steps, the result, the review points, and what good enough to go live means.
We build the agents and the skills and knowledge they need, from your examples.
We run them on real cases and tune them until the output meets the bar we agreed on.
They go live where your team works, with documentation and a walkthrough for the people who use them.
Scope
FAQ
A chatbot answers questions in a conversation. An agent does a defined job: it gathers what it needs, takes the steps, and produces a result, like a drafted proposal or an updated record.
A small team of agents that splits a bigger job. Each agent has one role, a coordinating agent passes the work between them, and a person reviews what matters. Fleets often run on a schedule, so the work is ready when you start your day.
Only where you decide they should. We place review points wherever a mistake would be costly, and most clients start with more approval steps than they end up needing.
We build in Claude, ChatGPT, Codex, Cursor, Gemini, and Grok Bot, using each platform’s own tools: in Claude, that means Claude Code, skills, and connectors. We pick the platform that fits the job and the tools you already use.
We agree on what good looks like before we build, then test against real examples until it meets that bar. Until it does, it doesn’t go live.
Agents use AI platform subscriptions or tokens, which you pay the provider for directly. Before we build, we estimate running costs against the hours the agent gives back, so you see that trade up front.
Get started
On the intro call, describe the work you’d hand to an agent, and we’ll talk about what it would take.