Single-agent workflows — one task, one agent, one outcome — cover most simple automation use cases. But enterprise workflows rarely fit this pattern. A customer support escalation might require a classification agent, a context-gathering agent, a resolution agent, and a quality-assurance agent all operating in sequence. A competitive intelligence workflow might require five research agents operating in parallel and a synthesis agent combining their outputs. These are multi-agent workflows, and they require different design patterns than single-agent implementations.
The simplest multi-agent pattern is the sequential chain: Agent A completes its task and passes structured output to Agent B, which completes its task and passes to Agent C. Sequential chains are easy to reason about and debug, but they do not parallelize work, and a failure in any agent blocks the entire chain. Use sequential chains when each step genuinely depends on the output of the previous step and when the workflow has a well-defined, linear structure.
When multiple tasks are independent, run them in parallel. Fan-out distributes work to multiple agents simultaneously; fan-in waits for all agents to complete and combines their outputs. A research workflow that needs to gather information from five sources runs five research agents simultaneously, then fans in to a synthesis agent. Parallel execution reduces total workflow time by the time of the longest individual task rather than the sum of all tasks. For research-heavy workflows, this difference is often 5 to 10 times.
For dynamic workflows where the number and type of sub-tasks cannot be determined in advance, the supervisor-worker pattern gives a high-level supervisor agent the ability to spawn, direct, and terminate worker agents based on evolving information. The supervisor maintains overall task state, determines what work needs to be done next, assigns it to appropriate workers, and synthesizes results. This pattern is powerful but requires careful design of the supervisor's decision logic and robust guardrails to prevent runaway task spawning.
For high-stakes decisions, running the same task through multiple independent agents and requiring consensus before acting provides a validation layer that single-agent execution cannot. Three agents classify the same input independently; the system acts on the majority result and flags cases where agents disagree for human review. Consensus patterns increase reliability at the cost of compute and latency — appropriate for decisions where errors are expensive, not for high-volume, low-stakes tasks.
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