Multi-agent systems, built with guardrails.
Some problems are too big for a single AI agent. We design systems where several agents work together, with retrieval over your private data, testing and monitoring built in. This is where our research and our commercial work meet. We do this work with university partners under Innovate UK, and we now do it for commercial clients too.
Get in touchHow orchestration composes specialised agents
D-PLUS, our third Innovate UK funded project, starts in October 2026 and is designed as a multi-agent system. One agent plans the learning scenario, another adapts it as the learner responds, and a third assesses performance and adjusts the difficulty. No single agent could do all three well.
The hard part of agentic systems is rarely the models. It is how agents pass work to one another, how errors are contained, how the system keeps running when one part fails, and how a person can look back afterwards and see what happened.
Every agentic system we build logs every agent call, tool use and intermediate result. This trace is not optional. It is what makes a system safe to run in production.
Frequently asked questions
When does a project need agentic engineering rather than a single agent?
When no single model can complete the task reliably from start to finish, or when parts of the task pull in different directions, for example speed against accuracy, or general reasoning against specialist retrieval. Splitting the work between specialised components is often more reliable than asking one model to do everything.
How do you evaluate multi-agent systems?
We agree an evaluation suite before we build: a set of test inputs and the outputs we expect. It runs with every release and you can see the results. The tests define what correct behaviour means for the system.
Do you offer RAG over private data?
Yes. We index your data in a vector store (Neon pgvector or Upstash Vector), and the system retrieves the relevant passages when it answers. Your data stays on your infrastructure, and the retrieval step can be audited.
How is this different from standard AI development?
Standard AI development usually builds one model for one task. In agentic engineering, models plan, call tools and hand work to other agents depending on intermediate results. Most of the engineering effort goes into coordination, error recovery and monitoring rather than the models themselves.