---
title: AI Agent
description: An AI agent is a system given a goal, a set of tools, and latitude to decide which tools to call and in what order — distinct from a workflow, which follows a path defined in advance.
url: https://www.factualminds.com/glossary/ai-agent/
publishDate: 2026-08-30
updateDate: 2026-08-30
---

# AI Agent

> An AI agent is a system given a goal, a set of tools, and latitude to decide which tools to call and in what order — distinct from a workflow, which follows a path defined in advance.

## Definition

An **AI agent** is a system given a goal, a set of tools, and the latitude to decide which tools to call and in what order to reach that goal.

The contrast that matters is with **workflow automation**, which follows a path defined in advance: if this, then that. A workflow handles the cases you anticipated. An agent handles the case you did not — the customer whose order shipped in two parcels, one scanned as delivered to a neighbour.

That flexibility is the entire value and the entire risk. It means the agent can also take an action you did not anticipate.

## The three things that make it an agent

1. **Tools.** Functions it can call that change or read the world — look up an order, check stock, issue a refund. A model with no tools is a text generator, not an agent.
2. **A loop.** It observes the result of a tool call and decides what to do next, rather than executing a fixed sequence.
3. **A stopping condition.** It decides when the goal is met, or escalates.

A retrieval chatbot that answers product questions has none of these. It is useful, and it is not an agent.

## Why the boundary matters more than the model

The most common production failure in agent systems is not a bad model output. It is an agent with more write access than anyone intended, discovered after it used it.

This is why in production the design order is: tool catalog first, authorization second, evaluation third, prompt last. Declare exactly which actions exist. Evaluate every write as a permission decision **outside** the model — a model can be argued out of a system prompt, but not out of an authorization policy. Then measure quality against a golden dataset with a pass bar before anything ships.

## Autonomy is per action, not per agent

A single agent can reasonably be fully autonomous on reads, semi-autonomous on low-value writes, and gated on anything that moves money. Answering where an order is needs no approval. Issuing a refund does. Changing a price across a category definitely does.

Setting one blanket autonomy level for a whole agent is how teams end up either shipping something dangerous or shipping something so constrained it saves nobody any time.

## When not to build one

- **The decision is already deterministic.** If a human follows the same rule every time with no judgement, that is a workflow. Workflows are cheaper, faster, and easier to audit.
- **The data cannot support it.** An agent reasoning over systems that disagree about identifiers produces confident, wrong answers. Fix the join keys first.
- **Nobody owns the outcome.** An agent without a named human owner accumulates edge cases until someone quietly switches it off.

## Related terms

[Tool catalog](/glossary/tool-catalog/) · [Human in the loop](/glossary/human-in-the-loop/) · [Agent evals](/glossary/agent-evals/) · [Model Context Protocol](/glossary/model-context-protocol/) · [Amazon Bedrock AgentCore](/glossary/bedrock-agentcore/) · [Agentic commerce](/glossary/agentic-commerce/)

## Related AWS Services

- ecommerce-ai-agents
- amazon-bedrock-agentcore
- generative-ai-on-aws

## Related Posts

- ai-agent-vs-workflow-automation-ecommerce-2026
- ecommerce-ai-agent-autonomy-spectrum-2026
- amazon-bedrock-agentcore-production
- multi-agent-ecommerce-operations-team-2026

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*Source: https://www.factualminds.com/glossary/ai-agent/*