How AI Agents Work

Short answer

An AI agent receives a goal or trigger, reads the context it is allowed to use, decides what action to take, calls tools when needed and returns an output.

IBM describes AI agents as systems that can autonomously perform tasks on behalf of a user or another system. Microsoft highlights that agents can choose which knowledge and tools to use at each step, which means testing and governance matter. OpenAI frames agents around use cases, tool design, orchestration and guardrails.

Sources: IBM AI agents, Microsoft AI agent adoption guidance and OpenAI practical guide to building agents.

The basic loop

1. A trigger starts the workflow.
2. The agent receives instructions and context.
3. The agent decides what it needs next.
4. It calls a tool, searches a source or prepares an output.
5. It checks whether the goal is complete.
6. It continues, asks for review or returns the result.

What the agent needs

• Goal
• Context
• Tools
• Memory when allowed
• Guardrails
• Review
• Logs

Agent versus chatbot versus automation

A chatbot responds inside a conversation. A traditional automation follows fixed rules. An AI agent can choose steps inside a defined goal and may use tools to complete the workflow.

A business workflow example

When a customer request arrives with a support ticket, billing history and product notes, an AI agent could summarize the issue, check for missing fields, prepare a suggested response and route the ticket to the right team.

The agent should not automatically approve a refund, change a contract or send a sensitive external message unless the workflow has strict approval gates and the team has tested those cases.

Readiness checklist

• What starts the workflow?
• What data can the agent read?
• What tools can it use?
• What action can it take without review?
• Where should it pause?
• Who approves the output?
• What record is kept?
• What counts as success?