AI agents, explained without the jargon

The plain definition

An AI agent is a system that pursues a goal across multiple steps, usually by calling tools or APIs along the way. That's it.

Stripped of marketing, an agent is: a loop that calls a language model, the model decides what to do next, executes something (search, read a file, send an email, run code), reads the result, decides the next step, and so on until it's done.

Everything else — "autonomous," "agentic," "reasoning" — is decoration. The core is: model + tools + loop + termination condition.

Workflow vs agent vs assistant

Three things often get conflated. Distinguishing them helps you decide what to build.

• Assistant. One prompt, one response. ChatGPT in chat mode. Useful, but every step needs you.
• Workflow. A fixed sequence of steps, each calling an AI model. "Draft, then critique, then rewrite" is a workflow. The path is deterministic.
• Agent. Same model + tools, but the model picks the next step. The path is dynamic, decided at runtime.

Workflows are easier to debug. Agents are more flexible. Most production systems are workflows pretending to be agents — and that's usually correct.

When agents are worth it

Agents make sense when three things are true:

1. The path can't be predicted. If you can map the steps in advance, build a workflow.
2. The downside of a wrong step is bounded. Agents will make wrong calls. You need to be okay with that, or have human review at the right points.
3. The task takes long enough that adaptivity matters. A 3-step task is rarely worth the complexity of an agent.

Concrete examples: research that has to follow citations wherever they lead; customer-support triage across many possible categories; multi-step debugging where the diagnosis affects the next test. None of these have a fixed script.

Where they predictably fail

Agents have well-documented failure modes. Knowing them is half of designing one well.

Goal drift. The agent loses track of what it was supposed to do and pursues a tangent. Mitigation: re-state the goal in every loop iteration.

Tool misuse. The agent calls the wrong tool with confidence. Mitigation: tight tool descriptions, validation of arguments, and a small toolset.

Overconfidence on retrieved data. The agent reads a search result and quotes it as truth. Hallucination detection applies inside agents too.

Infinite loops. The agent keeps trying the same thing in slightly different ways. Mitigation: a max-step budget and explicit termination conditions.

Prompt injection. Untrusted content (an email body, a webpage) tries to override the agent's instructions. Mitigation: separate trusted and untrusted contexts, never let user content set high-level goals.

Designing a safer agent

If you're building one, six principles get you most of the way:

1. Start with a workflow. Only switch to an agent when the workflow visibly fails.
2. Limit the toolset. Five well-named tools beat fifteen vague ones.
3. Put a human in the loop for irreversible actions (sending emails, making payments, editing live files).
4. Log everything. You will need to debug.
5. Set a step budget. Stop the loop after N iterations.
6. Have an explicit success criterion the model can check against.

This is the same mental model Iro AI's AI agents course teaches in 5-minute exercises — not how to write the code, but how to think about when and where agents help.