If the same input always follows the same path, you have a workflow, and building it as an agent buys you unpredictable cost, unpredictable latency and harder testing in exchange for flexibility you'll never use. Draw the steps on a whiteboard. If you can, build what you drew.

The Task Must Be Identical Every Time

Regulated processes, financial calculations, anything audited. An agent that chooses its own path will occasionally choose differently, and 'it usually does it the same way' is not an acceptable answer to a compliance question. Determinism is a feature here, and agents don't offer it.

Every Action Is Irreversible

If the agent can't do anything useful without taking an action you can't undo, you'll need approval on every step, and an agent where a human approves every step is slower and more annoying than a good form. Build the form.

You Don't Have the Traces to Debug It

Agents fail in ways you can only diagnose by reading the full sequence of tool calls. If your infrastructure can't capture and store traces, you will be unable to work out why anything went wrong. Fix that first, or don't ship an agent.

The Work Is One Step

A surprising number of agent projects are wrapping a single model call in loop machinery. If the task is 'summarise this' or 'classify that', you don't need an agent, and adding the loop makes it slower and less reliable for no gain.

Agentic AI and MCP for Product Managers

Module 1What 'Agentic' Actually Means

Module 2How an Agent Actually Works

Module 3MCP: What It Is and Why You Should Care

Module 4Designing the Tool Layer

Module 5Permissions and the Human in the Loop

Module 6Evaluating Agents

Module 7Cost, Latency and Unpredictability

Module 8Designing What the User Sees

Module 9Risk and Blast Radius

Module 10When an Agent Is the Wrong Answer

Module 11Final Project: Design One Real Agent End to End

Module 12Appendix: The Prompt Library

Five Situations Where a Workflow Wins