Multi-agent systems · orchestration
Multi-agent AI systems, used only where a process needs them.
When one process needs several AI agents working together, we design and run the orchestration, with human checkpoints and monitoring built in. When one well-built system will do, we build that instead, because it costs less to run and is easier to maintain.
What a multi-agent system is
An AI agent is a system that can reason about a task and take actions with tools you approve, such as reading a document, updating a record, or sending a message. A multi-agent system uses several specialised agents, each responsible for one stage of a process, coordinated by an orchestrator that passes work between them, retries failures, and stops at checkpoints for a person to approve.
When one system is enough
- The process has one main skill, such as answering questions from your documents or booking appointments
- Steps are short and follow a fixed order that ordinary software can handle
- Running cost and simplicity matter more than covering every edge case
When you need orchestration
- The process has distinct stages that need different skills, such as researching, drafting, checking, and approving
- Work has to run in parallel or across several systems and roles
- Each stage needs its own quality checks, and some need a person to sign off
- Failures must be retried or escalated without losing the work done so far
An example: one process, several agents
In our content pipeline, a research agent finds and scores topics, a scripting agent drafts the script, and the draft waits in an approval workspace until a person signs it off. Only then does the pipeline render the video. The orchestrator keeps each step in order, retries anything that fails, and records what happened at every stage.
Human checkpoints and monitoring
Every multi-agent system we build has approval gates for high-impact actions, audit trails across agents, evaluation of each agent’s output, and alerts when quality or cost drifts. People stay in control of the system.
Case studies
Questions
What is a multi-agent system, and when do I need one?
Most processes need one well-built AI system. When a process has several distinct stages that need different skills, for example researching, drafting, checking, and approving, we use several specialised agents coordinated by an orchestrator. We use that design only when the process needs it, because simpler systems cost less to run and are easier to maintain.
How much does AI automation cost, and how do you keep AI running costs low?
It depends on the process, so we quote a fixed scope after the audit rather than guessing up front. Most builds in our case studies took four to eight weeks. To keep running costs low we use the right model for each task, cache and route requests, and measure the cost per process, so you can see what each automated task costs to run.
Will AI replace my team?
No. We target the repetitive, time-consuming tasks that fill people’s days: copying data between systems, chasing documents, answering the same questions, compiling reports. Your team keeps the work that needs judgement, relationships, and exceptions, and every system we build has checkpoints where a person approves or takes over.
Tell us where time goes today. We’ll show you what we’d automate first.
