What Are Multi-Agent Workflows
A multi-agent workflow is a coordinated sequence of actions performed by multiple AI agents, each with a defined role. Instead of one agent attempting to do everything, the workflow divides the goal into subtasks and assigns each to the agent best suited for it. The result is often higher quality and more reliable than a single generalist agent.
Workflows can be linear (Agent A then B then C) or branching (a supervisor delegates to multiple specialists in parallel or sequence). The key is that handoffs are explicit: each agent receives structured input and produces structured output for the next step or for aggregation. Ambiguous handoffs lead to duplicated work, lost context, or contradictory outputs.
Multi-agent workflows sit at the intersection of agent orchestration and domain design. The orchestration layer manages execution; the workflow design defines the logical flow. Teams that get both right can scale from simple pipelines to complex, adaptive systems.
How Multi-Agent Workflows Work
The supervisor pattern is the most common. A supervisor agent receives the high-level goal, produces a plan with subtasks, and delegates each subtask to a specialist. Specialists might include a research agent (find information), a writing agent (draft content), a fact-check agent (validate claims), and a formatting agent (structure output). The supervisor evaluates each result and decides whether to continue, revise, or complete.
Pipeline workflows are simpler: each agent runs in sequence, with output from one becoming input to the next. A content pipeline might go: outline agent, draft agent, edit agent, publish agent. Dependencies are linear, and there is no branching. Pipeline workflows are easier to reason about and debug but less flexible than supervisor-driven workflows.
Collaborative patterns involve agents that debate or refine each other's work. One agent proposes a solution; another critiques it; a third synthesizes. This can improve quality for creative or analytical tasks but increases cost and latency. Use collaborative patterns when the quality bar justifies the overhead.
Use Cases for Multi-Agent Workflows
Customer support escalation is a natural fit. An intake agent triages and routes. A context agent gathers account history. A drafting agent writes the response. A quality agent checks tone and policy compliance. A human reviews before sending for high-risk cases. Each agent has a narrow scope; the workflow ensures they work together coherently.
Research and report generation benefit from multi-agent design. A search agent finds sources, a synthesis agent summarizes findings, a citation agent formats references, and a writing agent produces the final report. The workflow can include a fact-check step for claims that need verification. This structure is common in AI agents for research applications.
Content production pipelines use multiple agents for planning, drafting, editing, and publishing. Each stage can use a different model or prompt. The workflow enforces order and handles revisions. Similar patterns apply to business automation across marketing, sales, and operations.
Limitations and Safety
Multi-agent workflows add complexity. More agents mean more failure points, more latency, and higher cost. State management becomes critical: if the workflow crashes mid-execution, can it resume? Are there duplicate or conflicting actions? Design for idempotency and clear rollback paths.
Handoff quality matters. If Agent A produces messy output, Agent B may misinterpret it. Define schemas for handoffs and validate them. Use trace IDs so you can follow a request through the entire workflow when debugging. Consistent logging across agents is essential for monitoring and compliance.
Security considerations: each agent should have minimal permissions. The workflow orchestrator should not expose sensitive data to agents that do not need it. Audit logs should capture which agent accessed what and when. For regulated workflows, human review gates should be mandatory at defined points.
Get Started with AIACI
AIACI — Agents Creating Intelligence — provides guidance for building multi-agent workflows that scale with LLM-ready structure. Start with a simple pipeline before moving to supervisor patterns. Use AI agent builders to prototype, then harden with orchestration and monitoring. Download the AI Chat app to experience conversational AI and explore agent examples for workflow inspiration.