Quick Answer
A single AI agent is configured to do one thing well: analyze a competitor page, draft a blog post, or grade content against guidelines. A multi-step workflow chains multiple agents together, where each agent handles a specific part of a larger process and passes its output to the next. The first agent might research a topic, the second synthesizes findings into an outline, the third writes the draft, and the fourth checks it against brand guidelines. Workflows let you break complex work into specialized steps with human review points where they matter.
A single agent works like a specialist. You give it instructions, connect it to the tools and knowledge bases it needs, and it executes one type of task. A competitive analysis agent reads a webpage and returns structured insights. A content grader evaluates a draft against your scoring criteria. A meeting prep agent pulls account context and generates a briefing document. Each agent is focused, and that focus is what makes it reliable. You can refine the instructions, test the outputs, and trust it to handle that specific job consistently.
The limitation appears when work requires multiple distinct steps. Writing a research-backed blog post involves finding sources, evaluating them, synthesizing key points, creating an outline, drafting content, and reviewing against guidelines. A single agent trying to do all of that becomes unwieldy. The instructions get long and conflicting, the output quality becomes inconsistent, and there's no natural place for human review between stages.
A workflow solves this by orchestrating multiple agents in sequence. Each step has its own agent with focused instructions. The workflow passes outputs from one step to the next automatically. You can insert review steps where a human approves the research before drafting begins, or checks the outline before the full write-up. The Workflow Builder provides a visual canvas for designing these sequences, and Workflow Execution runs them with progress tracking and the ability to pause, review, and resume.
Workflows also support different step types beyond just chaining conversations. Loop Operations steps can process batches of data through an agent. Logic Operations steps can filter, combine, or transform data without using an LLM at all. AI Writing steps open a document canvas for longer-form editing. This flexibility means workflows can handle complex processes that mix AI generation, data transformation, and human judgment.
The decision between a single agent and a workflow comes down to complexity. If the task has one clear objective and the agent can deliver it reliably, keep it simple. If the work has distinct phases, benefits from specialization, or needs human checkpoints, build a workflow.
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