AI Agents
AI agents that do the work, not just the talking
A chatbot answers questions. An agent finishes tasks. We build autonomous AI agents that plan multi-step work, use your real systems and tools, and hand off to a human at exactly the right moment.
From conversation to execution
Most organisations have already experimented with a chatbot and discovered its ceiling: it can explain a refund policy, but it cannot actually issue the refund. The gap between answering and acting is where the real operational value sits, and closing it is what agentic AI is for.
An AI agent is a system that receives a goal rather than a script. It breaks that goal into steps, chooses which tools or APIs to call, observes what came back, corrects itself when a step fails, and either completes the task or escalates it with full context attached. That loop is what lets one agent absorb work that previously needed a queue of people.
We build these systems for production rather than for demos. That means every agent ships with defined boundaries on what it may touch, logging of every action it takes, a rollback path when something goes wrong, and a human approval gate on anything irreversible. An agent you cannot audit is an agent you cannot deploy.
What we build
Single-purpose task agents
Focused agents that own one high-volume workflow end to end — triage, data entry, reconciliation, report generation or first-line support.
Multi-agent systems
Coordinated fleets where specialist agents hand work to each other under an orchestrator, with a shared memory of what has already been done.
Tool and API integration
Agents connected to the systems you already run — CRM, ERP, ticketing, databases, internal APIs and third-party services — through typed, permission-scoped tools.
Retrieval and knowledge grounding
RAG pipelines so agents answer from your documents, policies and data instead of from a model’s general training, with citations back to the source.
Guardrails and human-in-the-loop
Approval gates, spend and action limits, content filtering, and escalation rules that route edge cases to the right person with full context.
Observability and evaluation
Traces of every step, evaluation suites that catch regressions before release, and dashboards showing what the agent handled versus what it escalated.
How we work
Agent projects fail when they start with the model instead of the workflow. We start with the work.
Map the workflow
We sit with the team that does the work today and document the real path — including the exceptions, the informal judgement calls and the steps nobody wrote down. This is where most of the value and most of the risk is found.
Pilot on a narrow slice
We pick one workflow with clear volume and a measurable baseline, then build an agent for that slice alone. A narrow pilot proves or disproves the case in weeks instead of quarters.
Harden for production
Guardrails, permissions, logging, evaluation suites and fallback behaviour. We test the failure modes deliberately — bad inputs, timed-out tools, ambiguous instructions — before real users meet them.
Deploy, measure, expand
The agent goes live alongside the human team with monitoring in place. Once the numbers hold, we widen its scope or apply the same pattern to the next workflow.
Where agents pay off fastest
Agents earn their keep on work that is high in volume, rule-dense and currently spread across several systems. If a task is rare or requires genuine human judgement, we will tell you it is a poor fit.
- Support triage — classifying, enriching and routing inbound tickets before a human sees them
- Operations reporting that today means pulling numbers from four systems into a spreadsheet
- Order, invoice and claim processing with clear rules but messy inputs
- Research and summarisation across large internal document sets
- Data reconciliation between systems that were never designed to talk to each other
- Onboarding and provisioning workflows that touch many tools in sequence
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot responds to a message and stops. An agent is given a goal and works toward it: it plans steps, calls tools and APIs, checks its own results, retries when something fails, and completes the task or escalates it. The practical difference is that a chatbot tells your customer how to reset a subscription, while an agent resets it.
Which AI models do you build on?
We are deliberately model-agnostic. We select from the current frontier and open-weight models based on the task, your latency and cost targets, and where your data is allowed to live. Because models change quickly, we build an abstraction layer so you can switch providers later without rewriting the agent.
How do you stop an agent from doing something harmful?
Through layered constraints rather than trust. Each agent gets a scoped set of tools and cannot act outside them; irreversible actions such as payments, deletions or external messages sit behind human approval; spend and rate limits are enforced in code; and every action is logged so any decision can be reconstructed afterwards.
Do we need our data in perfect shape before starting?
No, and waiting for perfect data is the most common reason these projects never start. We begin with the data you actually have. Part of the discovery work is identifying which gaps genuinely block the workflow and which are tolerable, so cleanup effort goes only where it changes the outcome.
How long before we see something working?
A narrowly scoped pilot typically reaches a working prototype within a few weeks, because we deliberately limit it to one workflow. Production hardening — guardrails, evaluation, integration and security review — usually takes longer than the prototype itself. We scope both honestly at the start rather than promising a date we cannot defend.
What happens when the agent cannot handle a case?
It escalates. Designing the escalation path is part of the build: the agent hands the case to a named human queue with everything it already gathered, so the person picks up mid-task rather than starting over. Escalation rate is one of the metrics we track, because a falling rate is how you know the system is improving.
Related services
Have a workflow that never seems to shrink?
Tell us what your team spends its week doing. We will tell you honestly whether an agent is the right answer, and what a first pilot would involve.