A multi-agent system is an architecture in which multiple AI agents work together, each responsible for a specific capability or subtask, coordinated by an orchestrating layer that routes work between them. Rather than asking a single agent to handle every aspect of a complex task, a multi-agent system decomposes the work across agents that are each optimized for their specific function.
Examples include research systems where one agent retrieves information, a second evaluates its relevance, a third synthesizes findings, and a fourth formats the output. In a Salesforce context, a multi-agent deployment might use one agent for case triage, another for knowledge retrieval, and a third for drafting customer communications, with an orchestrator managing the handoff between them.
Multi-agent systems are more powerful than single agents for complex, long-horizon tasks, and they enable parallelization of work that would be sequential in a single agent. They also introduce more complexity in design, testing, and governance. The interactions between agents can produce emergent behaviors that are harder to anticipate and control than the behavior of a single agent operating alone.
The governance implications of multi-agent systems are significant. When multiple agents share access to data and action surfaces, and their outputs feed into each other's inputs, errors can compound rapidly. Robust logging, clear accountability for each agent's actions, rate limits on high-risk operations, and human checkpoints at critical junctures are essential components of a safe multi-agent deployment.