GPTfy Glossary
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.
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).
See Multi-Agent Orchestration in GPTfy
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