What it is
A multi-agent system uses several purpose-built AI agents that collaborate on complex tasks. Instead of one monolithic agent trying to do everything, specialized agents handle their domain — a sales agent qualifies leads, a service agent resolves tickets, a scheduling agent books meetings — and they pass context to each other when tasks cross boundaries.
Why it matters
Real business processes span multiple departments. A customer inquiry might start as a support ticket, escalate to a sales conversation, and end with a scheduled demo. Multi-agent systems handle these cross-functional workflows without dropping context.
Key components
- Agent specialization
- Context handoff
- Orchestration layer
- Shared memory
- Escalation routing
How it connects
Agentforce supports multi-agent architectures through agent-to-agent handoffs. The A2A protocol and MCP extend this to agents built on different platforms.
Good to know
Start with single-agent deployments. Multi-agent systems add complexity in orchestration, context passing, and debugging. Get one agent working well before adding more.
Related terms
A2A (Agent-to-Agent Protocol)
Google's open protocol that allows AI agents from different vendors to communicate and collaborate with each other.
Agent Orchestration
The coordination and management of multiple AI agents working together to accomplish complex workflows that no single agent could handle alone.
Agentforce
Salesforce's AI agent platform that enables businesses to build, customize, and deploy autonomous AI agents across sales, service, marketing, and commerce.
Agentic Enterprise
An organization that has shifted its core business processes from manual workflows and traditional software to autonomous AI agents as the primary operating system.