AI Agent Development
An extra person on the team, working around the clock.
An agent takes a job off your team and finishes it, at 2am, on a Friday, and while everyone is in a meeting. It reads what came in, decides what to do, calls the systems it needs, and stops to ask when it hits something it should not decide alone. We build them on Claude and GPT, wired into the tools you already run, so the queue that used to wait for a free person stops waiting.
- Inbound email and WhatsApp read, classified and answered
- Quotes drafted from a spec and a price list, ready for a person to send
- Research that runs overnight so the answer is waiting at 8am
- CRM records kept current without anyone typing into two systems
- Approval queues where the agent explains what it wants to do and why
- Escalation to a person the moment confidence drops
What separates an agent from a chatbot
A chatbot answers questions. An agent finishes work: it has tools, permissions and a job with an end state. Ours get a narrow list of things they're allowed to do, credentials scoped to exactly those, and a log of every call they make. That log is the part clients end up caring about most, because it turns "the AI did something" into a line you can read.
Where we put the human
Every agent we ship has at least one point where a person signs off, and we choose it deliberately. Sending something to a customer, moving money, changing a record other systems read: those get a review step. Reading, drafting, classifying and enriching usually do not. As the team watches it get things right, that step moves or comes out, and you make that call with a few weeks of real numbers in front of you.
What you own at the end
The prompts, the code, the workflow definitions and the infrastructure config, in your repository and your cloud account. We would rather your team changed a prompt on a Tuesday without booking us, so handover is two working sessions and a runbook, and it's inside the price rather than beside it.
Questions we get asked
Which model do you build on?
Usually Claude, sometimes GPT, occasionally both inside one system where each is better at a different step. The choice lives in configuration, so changing it later is a setting and a round of testing.
What stops it doing something expensive by mistake?
Scoped credentials and an allow-list of actions. An agent that needs to read a calendar gets read access to a calendar and nothing else. Anything that costs money or leaves the building goes through a person first.
How long until one is running?
Most go live 4 to 8 weeks from the first discovery session. We build in production conditions from week 1, on real data and real integrations, because the distance between a sandbox demo and a working system is where these projects usually die.
What happens when the models get better?
You change a setting and re-run the test set. The business logic sits outside the model, so a new release is a controlled swap rather than a rebuild. That is the main reason we keep the two apart.
Tell us what's slowing your business down.
30 minutes. No pitch, no deck. Just listening to your needs and seeing how we can help.
Schedule a discovery call