Multi-Agent AI Systems

When one agent is not enough

A multi-agent system is a team of specialised agents that share one process. Each agent owns a step, such as intake, research, analysis, review, or reporting. They pass work to each other and stop when the process has a finished result. One model trying to do every step tends to skip checks. A team with a written handoff does not.

This is the right shape when the work is too wide for a single agent. A purchase request that must be read, checked against policy, matched to a supplier, reviewed, and turned into a report is five jobs. Giving each job to an agent that only knows that job keeps the mistakes local and the result traceable.

It is the wrong shape when the work is one task. Reviewing an invoice, extracting fields from a form, or answering a query from a known source is an AI agent, not a team. We will tell you which one you need on the discovery call, before a proposal is written.

How the agents coordinate

Each agent receives a defined input and returns a defined output. The next agent does not see the whole history of the company. It sees the package the previous step was allowed to pass on. That limit is what makes a review step useful. The reviewer is not the same agent that wrote the draft.

Coordination is part of the build, not an afterthought. We decide the order of the steps, what happens when a step fails, and who is told. A stuck agent should not silently retry forever, and it should not invent a result so the chain can continue. The process either finishes or it escalates.

The agents use the tools you already have. Intake might read a mailbox or a form. Research might read your records. Reporting might write back to the system your team opens every morning. We do not ask you to move the work into a new product so the agents have somewhere to live.

What you can inspect

You can see which agent handled which step, and what it passed on. That record is how your team checks a result without redoing the whole job. It is also how we debug a step that is wrong without guessing which part of a single prompt caused it.

Weekly demos during the build use your real data, so you are watching the handoffs and not a scripted example. The scope, the timeline, and the price are fixed before that build starts. Launch includes monitoring, documentation, and a handover so your team can run the system after we step back.

Where the records include personal or health data, the same rules apply as on our other builds. We follow GDPR and HIPAA requirements, use role-based access, and can deploy on your own infrastructure so the data stays in your environment.

What we need from you

We need the process as it actually runs, including the steps people skip when they are busy. We need one owner for each step, a sample of real cases, and the rule for a finished result. We need access only to the systems those steps already touch.

If nobody can describe what the next person is supposed to receive, the process is not ready for a team of agents. The discovery call is where we find that out. A fixed-scope proposal comes after, not before.