What Is An AI Agent, Actually?

A practical definition

An AI agent uses a model to choose actions and tools while working toward a goal. A workflow follows a more explicitly defined sequence. Both can include AI, conditions, and human review.

An agent does not need unlimited autonomy. For a business, clear permissions and a reliable stopping point are often more useful than letting it keep trying indefinitely.

Example: handling an incoming enquiry

A bounded lead assistant could read an enquiry, look up an existing CRM record, identify missing information, and draft a response. You might allow it to save the draft while requiring your approval before it sends a message or changes a deal stage.

The design should specify:

• Which tools and records it can access.
• Which actions need approval.
• What to do when information is missing or conflicting.
• Limits on retries, time, and spending.
• Where actions are logged and how to stop the workflow.

These behaviors must be built and tested. They are not automatic properties of a language model.

When an ordinary workflow is enough

If the steps are predictable, a standard integration or script may be simpler to operate. Moving a validated form submission into a CRM does not necessarily need an agent deciding what happens next.

Agent behavior is worth evaluating when the next step depends on varied context. Even then, compare it against a simpler baseline using examples from your actual process.

What about chat assistants?

A chat interface can be part of an agent system when it has tools and controlled actions. Being triggered by a person rather than a schedule does not decide whether something is an agent. The useful distinction is how it chooses and executes actions.

Anthropic’s guide to building effective agents explains the distinction between predefined workflows and model-directed agents.

See my AI agent service or discuss a workflow.