Agentic Build · Operations & back office · Hertfordshire & UK
AI agents that take the work off your plate — and actually finish it
Chatbots answer questions. Agents do the job. The Agentic Build is where I take one repetitive, time-hungry process in your operations and hand it to a well-built AI agent — one that reads, decides, acts and completes the task in your real tools, not a demo that breaks in week two. I design the architecture, wire it in with the right context and guardrails, test it hard, and stay to keep it running. Built narrow first, expanded once it's earning its keep.
What "agentic" actually means for your operations
An AI agent is software you give a goal to, along with the data and tools it needs to reach it. Where a chatbot produces words, an agent takes the next action — logging the enquiry, pulling details out of a document, updating a record, moving a job to the next stage — using the systems you already run on. In operations, that's the difference between AI that talks about your workflow and AI that quietly runs a slice of it. The Agentic Build is the careful engineering that makes that safe and reliable.
Where an agentic build pays off fastest
The strongest candidates are repetitive, rule-heavy tasks that steal a person's week: handling routine enquiries end to end, processing incoming invoices, forms and emails, keeping records tidy across systems, and chasing back-office work that nobody has time to remember. Each one is a whole category of admin that never really needed a human. We start with the single task that buys you back the most time, so the payback shows up early and clearly, before we talk about doing more.
How I build it so you can trust it
Because an agent can take actions, it has to be engineered, not just prompted. I design the agentic architecture around one clear task, connect it to your tools through the right integrations, and do the unglamorous context engineering that makes it accurate about your business rather than generic. Then come the guardrails: clear limits on what it can do on its own, a human in the loop on anything that matters, careful data handling, and hard testing against real situations before it touches a customer. Once it's live, it's monitored and tuned. That's what separates a dependable agent from a science project.
Start narrow, expand once it earns its keep
The widely-seen reason agent projects fail is trying to automate everything at once. I do the opposite. We build one agent, prove it in the real world, and only expand once it's visibly paying for itself. That keeps the first cost sensible, the risk low, and the benefit obvious. As your confidence grows, so does the system — each new agent added because the last one earned it, not because it sounded impressive.
Why me
I'm an agentic systems architect and a hands-on Claude and Gemini expert who builds this every day — MCP integrations, context engineering, evals, the lot. That means I know exactly where agents are genuinely reliable and where they're not, so I won't sell you an autonomous fantasy. You get someone senior designing and building the thing directly, keeping it in plain English, with no lock-ins and a real person to call when it needs changing. I've got your back after launch, not just up to it.
Straight answers
What's the difference between an AI agent and a chatbot?
A chatbot mainly produces answers in words — you ask, it replies. An agent can take the next action: updating a record, processing a document, sending a reply, or moving a task along, using the tools you've connected. That ability to act is the whole point, and it's exactly why an agent has to be engineered carefully with proper guardrails. Many useful builds do both — talk to a person, then actually do something about it.
How do you make sure an agent is safe to let loose on real work?
Guardrails first, always. I give the agent only the access it needs, keep a human checking anything that really matters, handle your data carefully, and test it hard against real situations before it goes anywhere near a customer. Once it's live it's monitored so problems get caught early. Built this way, an agent is safe to trust with a real slice of work — which is the only kind worth building.
What does an Agentic Build cost?
Builds are project-priced and scoped from a short discovery or an AI Opportunity Audit, because the right answer depends on the task and the systems involved. A first build typically lands somewhere from around £6,000 upwards, and it's phased so you prove value on the first agent before committing to more. You'll always have a clear scope and cost before anything starts.
Which of our processes should we automate first?
The one that's repetitive, rule-heavy, and quietly eating a person's week — often enquiry handling, document processing, or a back-office task that keeps slipping. If you're not sure, that's exactly what the AI Opportunity Audit pins down. We always start with the single highest-value task so the payback is early and obvious, rather than trying to boil the ocean.
Will this replace my staff?
The aim is to remove the dull, repetitive admin that wears people down, not the people. A good agent handles the category of work that never needed a human, so your team spends their time on customers and skilled work. I'm always upfront about where a person should stay firmly in the loop, and I design for that from the start.
Put an agent to work on your busiest process
Tell me the task that's eating your team's week. I'll show you the one agent worth building first — and build it properly.