AI Agents: what they are and when they make sense

A chatbot answers. An agent does.

The practical difference

A traditional chatbot takes a question and returns text. An agent takes a goal, decides the steps, uses tools (APIs, databases, browsers) and delivers a concrete result: an email sent, a meeting booked, an invoice processed.

Anatomy of an agent

• Model (the brain): usually a frontier LLM
• Tools (the hands): functions the agent can invoke
• Memory (the notebook): context that persists across steps
• Decision loop (the will): observe, think, act, repeat

When they make sense

When the task has many steps, clear rules and stable tools. Examples:

• Client onboarding (create account, send emails, book call)
• Market research (search, read, synthesize, compare)
• Back-office operations (reconciling data across systems)

When they don't

• High-stakes financial decisions without supervision
• Emotionally complex conversations
• Domains where one error costs 100× the productivity gain

The secret

Agents shine when we constrain them. Give them a small box with sharp tools, and magic happens. Give them the world, and they get lost.