AI agent

What it is

Anthropic draws the line this way. Workflows orchestrate models and tools through predefined code paths. Agents are systems where the model dynamically directs its own process and tool use. A practical version for business use:

• chat answers when you ask;
• an automation runs the same steps every time a trigger fires;
• an agent pursues an outcome and decides its own next step.

How it works

Anthropic says agents need “ground truth” from the environment at each step, such as tool results or code output, to assess their progress. They can pause for human feedback at checkpoints, and they usually have stopping conditions such as a maximum number of iterations.

Dan Martell teaches a similar four-step loop: diagnose the problem, assemble a plan and tools, take action, then assess the result and redo the work if it falls short. In his framing, without that last step you have an automation, not an agent. This is a teaching model, not a standard definition.

Benefits

• Owning outcomes, not tasks. As Martell puts it, chat buys back a little time, while an agent can take over a whole area of work.
• Handles variation. An agent can adapt when inputs don’t match the template, which is where fixed automations break.

Limits and risks

• Start simpler. Anthropic recommends the simplest solution that works, which may mean no agent at all. Agents trade latency and cost for better results on open-ended tasks.
• Compounding errors. Anthropic recommends extensive testing in sandboxed environments, with guardrails. The arithmetic is unforgiving: if each step is right 95% of the time and errors are independent, twenty chained steps all come out right only about 36% of the time. Ethan Mollick made the related point in August 2026 that accuracy drives how long a task an agent can do, so small gains compound. Give agents longer jobs only as their accuracy on short ones proves out.
• Permissions are the design work. Simon Willison frames agent design as deciding what the agent may touch, for example a staging key rather than production.
• Cost. Loops can run long and vary between runs, so cap retries and budgets.