Most "agents" are demos with a chat window. Our AI agent development work is built for the opposite: agents that call real tools, touch real systems, and are judged on whether they're still trusted after 90 days in production — not whether the demo went well.
Serving UK and EU clients: GDPR, EU AI Act, data residency, and VPC / on-premise deployment options are covered on our Trust & Safety page.
An AI agent is an LLM system that takes multi-step action on your behalf — querying systems, calling tools and APIs, making decisions about what to do next based on each result — rather than just generating a reply. That's the line between a chatbot and an agent, and it's also where most of the risk lives.
We build agents scoped to a specific workflow first: a defined set of tools, a defined set of permissions, and an evaluation harness that proves the agent behaves correctly before it ever sees real users. Scope expands once the first workflow is trusted in production — not before.
Every agent we ship goes through the same three checks before it touches production — regardless of how impressive the demo is.
The agent can only call the tools it needs for its specific workflow. No standing access to systems outside that scope, and no silent permission creep as the project grows.
An adversarial and edge-case test suite the agent has to pass before it goes live — and keeps passing as the underlying model or prompts change.
Anything irreversible — payments, deletions, customer-facing commitments — routes to a human checkpoint unless you explicitly tell us otherwise. See our Trust & Safety framework.
The pattern is the same across workflows: scope the agent to what it's actually authorized to do, and route anything requiring judgment to a human. Here's what that looks like for three of the most common requests we get.
Why they get escalated back to humans, and the tiered permission model that keeps a refund tool from exceeding its authority.
Why keyword-based screening filters out good candidates, and how semantic matching plus mandatory human review fixes it.
Why they stall at the approval step, and the spend-tier routing that lets them move fast without a compliance gap.
The questions we get asked most about AI agent development — answered straight, no sales pitch.
It depends on scope — a single-workflow agent with a few tools is a different project from a multi-step agent operating across several systems. We price by milestone, not by the hour, so you know the number before you sign. Book a call and we'll give you a real estimate within 48 hours.
Scoped tool permissions, explicit confirmation steps before any irreversible action, and an evaluation harness that tests the agent against adversarial and edge-case inputs before it ever touches production. Anything with real-world consequences gets a human-in-the-loop checkpoint unless the business case clearly says otherwise.
A chatbot converses. An agent acts — it calls tools, queries systems, chains multiple steps together, and decides what to do next based on the result of each step. Most of what people ask us to build is an agent wearing a chat interface.
On top of what you have, in almost every case. We connect agents to your existing APIs, databases, and internal tools rather than asking you to stand up new infrastructure first — see our LLM integration work for how that connection layer gets built.
Book a 30-minute call — we'll tell you honestly whether an agent is the right tool for your workflow, and what it would take to ship it safely.