Understanding AI Agents: Beyond the Chatbot

The term “AI agent” gets thrown around a lot these days, often with more marketing enthusiasm than technical precision. Let’s cut through the noise and look at what agents actually are, why they matter, and where they’re genuinely useful versus where they’re mostly hype.

What makes an agent different from a chatbot?

A chatbot responds to your input with text. An agent does something about it.

The distinction comes down to three capabilities that a basic language model doesn’t have on its own:

Tool use — the ability to call external functions, APIs, or services
Planning — breaking a complex goal into steps and deciding which to take next
Memory — retaining context across interactions and learning from outcomes

When you combine a large language model with these three capabilities, you get something qualitatively different from a chat interface. You get a system that can reason about a goal, figure out what actions to take, execute those actions, observe the results, and adjust its approach.

A practical example

Consider something simple: “Find me the cheapest flight from Sydney to Melbourne next Friday.”

A chatbot might tell you which airlines fly that route and suggest you check their websites.

An agent could search multiple booking APIs, compare prices, filter by your preferences (direct flights only, departure after 9am), and present you with actual options — or even book one if you’ve authorised it to do so.

The difference isn’t intelligence — it’s agency. The ability to act on the world rather than just describe it.

The architecture

Most agent systems follow a loop that looks something like this:

Observe → Think → Act → Observe → Think → Act → ...

In practice:

while not goal_achieved:
    observation = perceive(environment)
    plan = reason(observation, goal, memory)
    action = select_action(plan, available_tools)
    result = execute(action)
    memory.update(result)

The language model handles the “Think” step — it decides what to do next based on what it knows. The tools handle the “Act” step. And the loop continues until the goal is met or the agent decides it can’t proceed.

Where agents actually work well

After building with and observing various agent systems, here’s where I see genuine value:

Code generation and refactoring — agents that can read codebases, write code, run tests, and iterate based on results. This is arguably the most mature agent use case today.

Research and analysis — agents that can search multiple sources, cross-reference information, and synthesise findings. The key is that they can follow threads that a single query wouldn’t uncover.

Workflow automation — agents that handle multi-step business processes, making decisions at each step based on context. Think: triaging support tickets, processing invoices, managing approvals.

Where they struggle

Anything requiring genuine understanding of consequences — agents are confident actors, which makes them dangerous when the stakes are high. A human should always be in the loop for decisions that are expensive to reverse.

Long-horizon planning — current agents are much better at tactical execution (do this, then this, then this) than strategic planning. They can follow a plan, but generating a genuinely good plan for a complex problem is still hard.

Reliability at scale — when an agent needs to perform the same task thousands of times with high consistency, the stochastic nature of language models becomes a liability. Sometimes you just need deterministic code.

The honest take

AI agents are a real and useful technology. They’re not magic, and they’re not going to replace human judgment any time soon. What they do is extend what a single person can accomplish — they’re leverage, not replacement.

The best agent systems I’ve seen are the ones that stay humble: they do what they’re good at, they ask for help when they’re uncertain, and they keep a human informed about what they’re doing and why.

That’s the kind of agent I want to build with. More on that in future posts.