Platform
Multi-agent systems
Not one bot, but a team of agents that talk to each other — inside your business and across companies — to run whole workflows end to end.
Most automations are a single agent doing one job. The bigger wins come when several agents work as a team — each an expert, talking to one another to get something done end to end.
From one agent to a team
A single agent can answer a question or complete a task. A multi-agent system splits the work: an orchestrator understands the request and routes it to the right specialist agents, which can in turn call others. The agents share context and pass results back, so a whole workflow runs without a human stitching the steps together.
Two kinds of communication
Internal — agents inside your business
Specialist agents you own talk to each other behind one front door. A support orchestrator might hand a conversation to an orders agent, a returns agent, or a scheduling agent — each with its own tools and knowledge, sharing the same context.
External — agents across companies
Increasingly, your agents also talk to other organisations' agents. Your procurement agent can ask a supplier's agent for prices and availability; your booking agent can negotiate a slot with a partner's system — machine to machine, over shared protocols, with humans in the loop only when needed.
What it looks like in practice
- Customer-service orchestrator: a lead agent triages each message and hands off to specialists for orders, returns, billing, or tech — each an expert, sharing the same conversation context.
- Sales + operations: a sales agent qualifies a lead, then coordinates with an inventory agent and a scheduling agent to confirm an order and a delivery slot in one flow.
- Cross-company procurement: your buying agent automatically requests quotes and stock levels from a supplier's agent and compiles the options for you.
- Back-office pipeline: an intake agent extracts data from documents, a validation agent checks it, and a bookkeeping agent files it — with no manual handoffs.
The technology
Under the hood it's an orchestrator/worker pattern: a coordinating agent plans and delegates, worker agents execute with their own tools, and they exchange messages and share memory so context isn't lost between steps.
- Orchestrator and worker agents, each with a focused role and its own tools
- Shared memory and state so context carries across the whole task
- Message passing between agents, with results fed back to the coordinator
- Tool access over open standards (such as MCP) and emerging agent-to-agent protocols for cross-system communication
- Guardrails and human checkpoints on the steps that matter
Why this matters now
AI is moving from single chatbots to teams of cooperating agents, and the standards that let them talk — to your tools and to each other — are maturing right now. The businesses that map their workflows to agents early build the integrations, data, and operating habits that compound later. It's the right moment to start small and grow.