AI Agent Development for Real Systems
We build AI agents that plug into your existing systems and handle real workflows, not one that impresses in a demo and stalls the moment it meets your data and your teams.
Our Scope
What AI agent development covers
An AI agent is software that takes in information, makes a decision within limits you define, and acts inside your systems, without a person doing each step manually. Agent development is the work of designing, building, integrating and governing that agent so it holds up in production, not just in a demo.
We build agents across:
Customer and internal support — agents that answer queries and resolve requests, and know when to hand off to a person
Workflow and process agents — agents that execute multi-step tasks across departments and systems
Decision-support agents — agents that analyse options and recommend an action, with a person approving it
Knowledge agents — agents trained on your documents, policies and internal knowledge to answer accurately
Integration and orchestration agents — agents that connect and coordinate actions across your existing tools without ripping them out
Why most AI agents don't make it to production
Most AI agent projects do not fail because the model is weak. They fail because the agent was built before anyone checked whether it should have access to the data it needed, whether the systems it had to talk to would let it in, or who was accountable when it got something wrong.
A chatbot demo that reads well in a meeting is not the same as an agent handling real customer accounts, real approvals or real money. The gap between the two is integration, governance and the infrastructure work nobody demos.
We build agents against your actual systems and your actual governance requirements from the start, so the thing you approved in the design phase is still what is running six months later.
Use Cases
When you need AI agent development
Most organisations come to us for one of these reasons:
You have completed an AI readiness assessment, with us or elsewhere, and have a defined, scoped use case ready to build
A repetitive, high-volume process is consuming staff time that could go to higher-value work
Customer or internal support queries are growing faster than your headcount
You have data or systems that could inform a decision automatically, but nothing currently pulls them together
A previous AI agent pilot stalled in production and needs a rebuild that accounts for governance and integration
You need an agent that works across multiple systems, CRM, ERP, DMS, HR, finance, rather than inside just one of them
Common Obstacles
Common AI agent challenges and how we prevent them
These are the five failure modes we see most often, and the controls we put in place against each.
Agent works in testing, fails in production
We test the agent against your live systems and real edge cases, not curated sample data, before anything ships.
No clear boundary on what the agent can decide
We define what the agent can act on autonomously, what needs approval, and what it must escalate, before we write any code.
Integration breaks under legacy system constraints
We map every system the agent needs to reach during design, so rework appears as a costed line rather than a mid-build surprise.
No one owns the agent's outputs
We define who reviews agent decisions, who is accountable when it is wrong, and the escalation path, as part of delivery, not an afterthought.
Agent trained on the wrong or stale data
We build from your live data sources and set a refresh and validation cycle so the agent does not quietly drift out of date.
Our Methodology
Our AI agent development process
Four phases across a focused engagement. Each ends with something you can act on.
Discovery, use case definition
Phase 1We define exactly what the agent will do, which systems it touches, what data it needs, and what “working” looks like in numbers.
You get: A scoped use case with defined success metrics.
Design, architecture and governance
Phase 2We design how the agent connects to your systems, what it can decide alone, and what needs human approval, before any build work starts.
You get: An architecture and governance model signed off before development begins.
Build, integration
Phase 3We build the agent, connect it to your live systems, and test it against real workflows and edge cases, not a curated demo dataset.
You get: A working agent tested against your actual data and systems.
Pilot, monitoring and handover
Phase 4We run a scoped pilot with defined success criteria, monitor its decisions, and hand over with the documentation and ownership model your team needs to run it.
You get: A live agent with monitoring in place and a clear owner on your side.
Platforms and systems we build on
We are not tied to one model provider or agent framework, so the model behind the agent is chosen for your use case, not for what we happen to resell.
Cloud
Microsoft Azure, AWS, Google Cloud
Data
SQL Server, MySQL, PostgreSQL, MongoDB, warehouses and data lakes
Business systems
Microsoft 365, SharePoint, Dynamics 365, Salesforce, and CRM, ERP and DMS platforms generally
Integration
MCP servers and API-based integration, so agents connect to your systems without a rebuild
Security, compliance and governance
Lawful basis under UK GDPR for the personal data an agent would access or act on
Scoped access: the agent gets the permissions the use case needs, not blanket system access
Audit trails for every decision or action the agent takes
Human approval and escalation paths built in for anything the agent should not do alone
Data residency and vendor handling: where data is processed, and by which models
Value Delivered
What you get
An agent built against your real systems and real data, not a demo environment
A defined boundary for what it can decide alone and what needs a person
Integration into your existing tools without a system rebuild
A governance and ownership model so someone is accountable once it is live
Monitoring in place so drift or failure gets caught early, not discovered by a customer
A pilot with agreed success metrics before you commit to scaling it
How long AI agent development takes, and what drives the cost
Typically six to twelve weeks for a single, well-scoped agent. Longer for multi-agent or multi-system builds.
Four things drive both timeline and cost:
— how many systems the agent needs to integrate with
— how much of the agent's decision-making needs to be autonomous versus human-approved
— whether it needs cleanup before the agent can use it
— whether this is a new use case or a rebuild of a stalled pilot
FAQ
AI agent development FAQs
Not always. If you already have a clearly scoped use case, clean-enough data and know which systems are involved, we can start design directly. If any of that is unclear, we would recommend a readiness assessment first, it is cheaper than finding the gaps mid-build.
Every agent we build has a defined escalation path and a human owner. We set the boundary of what it can decide alone before build starts, so a mistake has a bounded impact and a clear point of correction.
Yes. We can assess an existing build or pilot, work out why it stalled, and take it forward, or rebuild the parts that do not hold up, rather than starting over.
We are not tied to one vendor or model. We choose the model and framework based on your use case, data sensitivity and existing systems, not on what we resell.
Yes, where the use case needs it. Most organisations start with a single, well-scoped agent and expand once it is proven, rather than building a multi-agent system on day one.
A chatbot answers questions. An agent can take an action inside your systems, update a record, trigger a workflow, escalate a case, within limits you define. Not every use case needs an agent; sometimes a chatbot or a simpler automation is the right, cheaper fix, and we will tell you if that is the case.
Build an agent that survives contact with your systems
Most AI agents fail quietly in production, not in the demo. We build agents against your real data, real systems and real governance requirements from day one.