AI Agent Development
An agent is a model with the ability to act, which makes it useful and makes it dangerous. The engineering that matters is not the reasoning loop — it is the permissions, the audit trail and knowing exactly where a human has to say yes.
Discuss your agent
What We Build
Agents doing real work inside real systems, with the controls that makes it safe.
Tool-Using Agents
Agents that call your APIs, query your data and operate your systems through a defined tool surface.
Workflow Automation
Multi-step processes — triage, research, data entry, reconciliation — handled end to end with checkpoints.
Support Agents
Agents that resolve tickets rather than deflect them, with an honest handover when they cannot.
Research Agents
Gathering, comparing and summarising across sources, with citations back to the original material.
Multi-Agent Systems
Specialised agents coordinating on larger tasks — used where it genuinely helps rather than for its own sake.
MCP & Integrations
Connecting agents to your tools through standard protocols rather than bespoke glue for each system.
What Production Agents Need
Observability
Full traces of every step, so a wrong answer can be diagnosed rather than argued about.
Approval workflows
Pause, present the intended action, and wait — with the context a human needs to decide quickly.
Retries and recovery
Failure handling that does not repeat a side effect twice, because agents retry.
Budget limits
Token, time and cost ceilings per run so a loop cannot spend without bound.
Injection defences
Untrusted content treated as data, not instructions — the most common agent attack.
Evaluation harness
Scored test scenarios so changes to prompts or tools can be verified before release.
Where We Put AI To Work
We take on AI projects where the outcome can be measured — a cost that falls, a queue that clears, a decision that gets more accurate.
Talk to our teamAdvanced AI Engineering Capabilities
The difference between a demo that impresses and a system you can rely on.
Evaluation harness
A scored test set built from your real data, run on every prompt, model or retrieval change.
Guardrails
Input and output filtering, prompt-injection defences and refusal behaviour you have chosen.
Cost engineering
Caching, model tiering and context trimming so unit economics work at real volume.
Provider abstraction
One interface across providers, so switching model is configuration rather than a rewrite.
Self-hosted options
Open-weight models served in your own infrastructure where data cannot leave your estate.
Full request logging
Prompt, context, response and score retained for debugging and audit.
How We Deliver
Start narrow, prove it, then widen the agent's remit.
Pick one workflow
A single, well-bounded task with a measurable outcome and a clear owner.
Build the tool surface
APIs the agent can call, scoped and permissioned, with an evaluation set.
Run supervised
The agent proposes, a human approves, and we measure how often it was right.
Widen autonomy
Approval gates removed only where the record justifies it.
Automate A Workflow Properly
Tell us which process eats your team's time. We will scope an agent for it, with the controls that make it safe to run.
Talk to our AI teamAI Agent Questions
Everything you need to know about our ai development work.