Multi-Agent Collaboration
In one sentence
Divide a complex task among multiple specialized AI agents, coordinated by an orchestrator agent that assigns tasks and compiles results.
When a single AI agent handles everything, prompts become increasingly long and error-prone. Splitting work across multiple agents—such as one for research, one for drafting, and one for editing—keeps rules clear and focused.
More AI agents mean more handoffs and potential points of failure, so log the inputs and outputs of every step for easy troubleshooting.
Example
Competitor analysis example: a research agent gathers data, an analysis agent builds comparison tables, a writing agent drafts the report, and a review agent verifies data sources.
Related terms
- AI Agent
- An AI that can read files, call external tools, and complete multi-step tasks on its own, unlike a standard chat AI that only replies with text.
- Workflow
- Break a task into fixed steps and assign them in sequence to AI or humans, with predefined inputs and outputs for each step.
- Human-in-the-Loop
- Keep human approval checkpoints within AI-driven workflows: humans make critical decisions, while AI handles preparation and execution.
