Agentic workflow
An agentic workflow is a process where an AI agent — not a human — plans the steps, calls tools or APIs, checks intermediate results and iterates toward a goal. It is fundamentally different from a chatbot that just answers a single question.
An agentic workflow turns a model from a question-answerer into an actor. Instead of returning a paragraph of text, an AI agent decides what to do next, picks a tool, runs it, reads the result, and chooses the next move. The plumbing that makes that possible is function calling and increasingly the Model Context Protocol, which standardise how the model reaches out to your systems.
The honest take is that this is also where the engineering gets hard. An agent that can take actions can also take wrong actions — send the wrong email, hit the wrong endpoint, loop forever, or quietly burn through tokens. The work shifts from prompt-writing to guardrails: scoped tools, retries, budgets, human-in-the-loop approval on anything irreversible, and good observability so you can see what the agent actually did.
The rule of thumb is that an agentic workflow is a product, not a prompt. Reliable behaviour comes from the same MLOps discipline that separates a useful AI feature from a risky one: evals before launch, monitoring after, and a clear story for what happens when the agent is wrong. Skip that and the demo looks magical while production quietly breaks.
