We build AI agents for a living, so we have a commercial incentive to tell you that every problem needs an agent. We are going to do the opposite: here is a clear framework for when not to build an agent.
The agent overhead
Agents are stateful, non-deterministic, and hard to test exhaustively. They require prompt engineering, tool definitions, escalation logic, observability infrastructure, and ongoing evaluation. The overhead is real — typically 3–4 weeks of engineering time to build correctly and ongoing maintenance to keep reliable.
When a simpler solution is better
Fixed-sequence automation — If your process always follows the same steps in the same order, a workflow tool (Zapier, Make, n8n) is faster, cheaper, and more reliable. Agents are for processes where the steps vary based on what the agent observes.
Single-step tasks — If the task is "classify this email" or "extract these fields from this document," a direct API call to an AI model with a structured output schema is simpler and more predictable than a full agent system.
Low-volume processes — An agent that handles 10 tasks per month is hard to justify economically. The break-even for agent overhead is usually somewhere around 50–100 tasks per month, depending on the complexity and the alternative cost.
High-stakes, irreversible actions — If an error means sending a wrong payment, deleting customer data, or making a binding commitment, the process probably needs human review at each step. Agents are not appropriate as the sole decision-maker in these workflows.
The decision sequence
Before building an agent, ask in this order: Can this be solved with a script? Can it be solved with a workflow tool? Can it be solved with a single AI model call? If all three are no, then an agent is probably the right tool. If any are yes, use the simpler option — and come back to agents when the process has grown complex enough to justify them.