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What Are AI Agents and Agentic Workflows: How They Differ from Ordinary Workflows, and When Not to Use Them

Guide ~8 min Updated 16 June 2026

Agents & Workflows AB103

For organizations that hear “AI agent” everywhere but still wonder what it really means

Through 2025 and 2026 the term “AI agent” has been used so much its meaning has blurred. This article cuts through the hype with a definition from a primary reference (Anthropic, Building Effective Agents): how an agent differs from an ordinary workflow, what kind of loop it runs, and most importantly, when you should not use an agent.

Workflow vs agent: the difference is “who controls the path”

Put simply, a workflow means you lay the track to follow, while an agent chooses the route itself.

How an agent runs as a loop

  1. Receives a task from a person
  2. Plans and acts on its own
  3. Calls a tool and reads the result
  4. Uses real feedback from the environment to assess progress at every step
  5. Repeats until it reaches an answer, or until guardrails tell it to stop

The key point is that an agent gets ground truth from the environment at every step rather than guessing blindly, and it must connect to tools (linking to external systems through standards such as MCP), knowledge/memory, and skills before it can do real work.

⚠️ When you should NOT use an agent

This is the heart of what the hype usually skips:

The principle from Anthropic: start with the simplest thing that solves the problem, and add complexity (workflow, then agent) only when it is genuinely necessary.

Summary for decision-makers

An agent is not an “upgrade” that is always better than a workflow. It is a different kind of tool for tasks whose path is uncertain. Before investing in building an agent, ask whether an ordinary workflow is enough for this task, and what will contain it if it goes wrong.


updated 16 June 2026