Many projects that use "agents" don't need them. A deterministic workflow, a fixed sequence of LLM calls and function calls, is simpler, more reliable, and usually cheaper. The question isn't "should I use AI" but "should this be agentic."
A workflow is a known sequence of steps. "Extract entities, then summarize, then categorize, then send notification." The steps don't change based on results. An LLM runs each step, but flow is hardcoded.
Workflows win when:
Agents win when the flow depends on what the agent discovers. Research, exploratory debugging, customer support that can branch into many directions, tasks where the "right next step" depends on what you just learned.
Agents win when:
Most production systems are workflows with agent steps embedded. A workflow orchestrates the pipeline; at specific junctures, an agent handles bounded exploration.
Example: a deterministic email ingestion pipeline calls an agent to classify and act on "complex" emails that don't match known categories. Workflow + agent hybrid.
Wrapping deterministic logic in an agent adds cost, latency, and failure modes without benefit. If your agent's "reasoning" is always "call tool X, then call tool Y, then respond", you have a workflow, just an expensive one.
If the steps and their order are fixed and you can draw the flowchart in advance: workflow. If the flowchart has decision points the LLM must make at runtime based on information it doesn't have yet: agent.