Anatomy of an agent workflow
A workflow begins with a goal or task description passed to the agent. The agent enters a reasoning loop: it reads its current context, decides what action to take, executes that action (typically a tool call or sub-agent invocation), receives the result, updates its context, and decides the next step. This loop continues until the task is complete, a stopping condition is reached, or a human handoff is triggered. The output of the workflow is whatever the final step produces — a document, a database entry, a completed transaction, or a structured summary.
Single-agent vs multi-agent workflows
Single-agent workflows use one reasoning loop with access to multiple tools. They are simpler to build and debug but can struggle with very long tasks where the context window fills, or with tasks that require genuinely parallel execution. Multi-agent workflows divide work across specialized agents — a planner that breaks down tasks, executors that handle specific tool categories, a reviewer that checks outputs. Multi-agent architectures scale to more complex tasks but introduce coordination overhead and new failure modes at the boundaries between agents.
When to use a workflow vs a direct LLM call
Workflows make sense when the task has more steps than fit comfortably in a single prompt, when different steps require different tools or capabilities, or when intermediate results need to be validated before proceeding. A direct LLM call is almost always better for tasks that fit in a single exchange — it is cheaper, faster, and easier to reason about. The overhead of a workflow is only worth paying when the task genuinely requires it.