Agent Workflow

Single-shot calls work for short tasks, but many real tasks require decomposition, observation, and revision. If a system cannot inspect an intermediate result and decide whether to search again, call another tool, or stop, it breaks down on longer business workflows.

Once tool use became common, teams realized that real tasks often required more than one call. A model might need to inspect a result, change course, or recover from an intermediate failure. That pressure produced explicit model-tool loops now described as agent workflows.

A user goal and current state are given to the model, which chooses the next action. Runtime code executes that action and returns an observation, the intermediate result of that action. The state is updated, and the model decides again whether to continue or stop. Token budget, tool permissions, maximum steps, and fallback behavior keep the loop bounded.

Agent workflow and tool use both let a model act beyond plain text, but tool use is the single capability while agent workflow is the control loop that repeats and coordinates those capabilities. If one lookup or action is enough, tool use is usually sufficient. If the next move depends on what just happened, agent workflow becomes relevant. Turning deterministic flows into agents too early often adds more control cost than value.

The biggest gain from agent workflow is that the system can revise its path after each intermediate result instead of committing to one shot. The cost is higher latency, more token spend, more chances to misuse tools, and significantly harder debugging. It is strongest when the next action truly depends on the previous observation, but fixed workflows are usually safer when the path is already known.

Teams use this for research assistants, multi-step support, and operational workflows that require branching decisions. But not every task needs an agent. If the path is already known and deterministic, a fixed workflow is usually safer and easier to operate.