Autonomous AI Agents

A chatbot or copilot is driven by a person typing in a prompt. Someone has to open the tool, write a prompt, check the answer and act on it.

Autonomous agents run on their own, they are started by an event or data in your systems, such as a new enquiry or a document arriving and they completes a defined job end to end with validation and verification built in. Your team review outcomes and exceptions instead of driving every step.

The best candidates are jobs that run often, follow a recognisable pattern and draw on data you can reach, but still take up skilled people’s time.

Classifying and routing requests, extracting details from documents, checking records and drafting responses are common examples.

Our discovery phase scores every opportunity on value, volume, feasibility, data readiness and risk, so you can see why one job comes before another.

It’s all about driving performance and human trust in the system. For people to adopt and see the benefits from the system it has to perform at a consistently high level.

Large, general-purpose agents asked to do everything tend to do none of it particularly well or drift more substantially over time. When they go wrong it is hard to see why and trickier to correct.

We break each job into tasks and give each task its own agent with one brief and one benchmark. Each agent can be tested, costed and improved on its own, which makes the whole system easier to trust.

Every agent has a KPI’s agreed in the design phase. Before go-live we test with real examples from your systems to ensure that it meets that benchmark.

Once live automated LLM judges review outputs against your service standards and flag any drop in quality. Our team consistently reviews results ensuring that you have full visibility of performance.

AI agents are not deterministic so variations will appear and this is a key consideration from the offset, it is baked into our flow designs from the very outset.

The risk of a wrong output is scored at the start and we use it to decide where people stay in the loop, where traditional software is needed and where additional loops can be trusted.

Exceptions are routed to a person rather than guessed at and evaluations catch problems early. Our agentic architecture means each agent does one task and therefore risk is further limited and faults can be traced to one place and fixed quickly and easily.

No. Part of the process is an assessment of your data, including its quality, availability and if APIs for data already exist.

A clear list of what needs to be done to connect each source is created, which becomes the technical scope for the build. Our team will then work with you to build required API’s/MCP services to enable the platform to succeed.

We have been building bespoke software for global enterprises for nearly two decades and as with all software we build security is designed from the offset.

During the proof of concept, agents work on static data extracted from your systems, so nothing touches live systems. In production, each agent can only reach the specific tools and data its task needs, through connections we build and control. Guardrails and limitations are imposed throughout to ensure that everything runs in line with agreed rules.

Running costs depend mainly on how often the job runs, the model required for good result and how much model processing each run needs.

We measure the cost per run during the proof of concept, so you can weigh it against the value before you commit to a build.

Once live our team tracks running costs and keeps tuning prompts and tools to bring them down.