Agentic AI Development
AI that does real work in your operation. Built prototype-first, with the guardrails and testing production demands.
Working software, not a demo
Most AI initiatives stall between the impressive demo and anything a business can rely on. The gap is engineering: data, integration, guardrails, evaluation - the unglamorous work that turns a capability into a system.
We build agentic workflows into our own delivery every day, so we know where they save real effort and where they don't. We'll tell you which is which before you spend.
Every engagement starts with one real workflow, proven on your data, not a platform programme.
This page is about agents doing work in your operation. For agents inside your engineering team, writing and testing code, see AI-Assisted Engineering.
From first proof to production
Prototypes and PoCs
A fixed-scope proof on one real workflow, using your data. Working software in weeks, and an honest read on whether it's worth taking further. More on prototypes and PoCs.
Agentic AI Development
Agents that handle multi-step operational work (triage, document processing, checks and reconciliation), acting within guardrails, with people overseeing what matters.
Production AI
Evaluation, monitoring, fallbacks and support: AI treated like any production system, because that's what it becomes the day your operation depends on it. More on production AI.
Grounded in real operational data
AI is only as good as the data and integration underneath it, which is why this work sits alongside our integration and data practice. A recent build includes real-time capture from AI detection equipment on refuse trucks and a weekly automated export of over 1.1 million records.
How an engagement runs
An agent is production software with more ways to go wrong, so the engagement is shaped around limits, evidence and monitoring.
Agree the boundaries first
Discovery with your technical and operational teams maps the workflows, data sources, system dependencies and the points where people stay involved. Before a line of code is written, we've agreed where the agent acts on its own, where it must ask, and where automation isn't worth it.
Build it traceable
Agents plug into the systems you already run: APIs, databases, internal tools and third-party platforms. Every action is traceable and explainable, approval workflows and audit trails are built in, and we test failure scenarios and edge cases before deployment - with rollback ready if it's ever needed.
Watched, measured, improved
After go-live we monitor the agent against the success metrics agreed up front, catch drift early and refine or extend it as your requirements change. The measure of success is the agent your operation still relies on a year later.
Common questions
What is agentic AI, practically?
Software that uses AI to carry out multi-step work (reading, deciding and acting within guardrails you set) rather than a chatbot that answers questions. Think triaging inbound work, processing documents, or keeping systems reconciled.
How do we start without betting the business?
With a proof of concept on one real workflow, at a fixed scope. You see it working on your own data in weeks and decide about production with evidence, not a slide deck.
How do you keep an AI system safe and reliable?
Guardrails on what it may do, human sign-off where actions have consequences, and evaluation and monitoring in production. It gets tested and validated like any other system we ship.