Multi-Agent Orchestration
A pattern where multiple AI agents — each with specialized roles and tools — collaborate to complete a task, coordinated by an orchestrator agent or framework.
Quick answer
What is Multi-Agent Orchestration?
A pattern where multiple AI agents — each with specialized roles and tools — collaborate to complete a task, coordinated by an orchestrator agent or framework.
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A single AI agent is good at focused tasks. Multi-agent systems shine when a goal requires diverse expertise: a "research" agent searches the web, a "data" agent queries Salesforce, a "writer" agent drafts an email, a "review" agent checks compliance. An orchestrator routes between them based on the task state.
Production frameworks: LangGraph, CrewAI, Autogen, Salesforce's own multi-agent orchestration in Agentforce. The trade-off: more agents = more LLM calls = more cost and latency, but better task decomposition for complex goals.
For Salesforce, multi-agent patterns are emerging in: complex case resolution (knowledge agent + customer agent + escalation agent), end-to-end sales workflows (research + outreach + scheduling agents), and account planning (a "deep research" pipeline that builds account briefs from multiple sources).
Related terms
Browse all terms- AI AgentA software entity that takes goal-directed actions autonomously, calling tools, retrieving data, and producing outcomes without step-by-step human guidance.
- Agentic AIAI systems that autonomously pursue goals using tool calls and multi-step reasoning — distinct from generative AI (which produces content) or predictive AI.
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