AI Agents: What They Can Actually Do

An AI agent is a language model that doesn’t just reply — it acts. Give it a goal, and it plans, calls tools, observes the results, and decides what to do next, looping until the job is done. “Book me a flight,” “fix this failing test,” “research these five companies” — agents aim to handle the whole task, not just the first sentence. This guide separates what works today from what’s still a demo.

The core loop

Every agent runs a version of the same cycle:

1. Think — the model reasons about the goal and the current state.
2. Act — it picks a tool and calls it (search the web, run code, query a database).
3. Observe — it reads the result.
4. Repeat — it decides whether the goal is met or another step is needed.

The language model is the brain. The tools are the hands. The loop is what turns a chatbot into something that can get things done.

Tools are the whole game

An agent is only as capable as the tools it can reach. Connect it to a code interpreter and it can run scripts; to a browser and it can navigate sites; to your calendar and it can schedule. The emerging standard for wiring tools to models is the Model Context Protocol (MCP), which gives agents a consistent way to discover and call external systems.

Without tools, an “agent” is just a chatbot with extra prompting.

Memory and planning

Two ingredients separate a robust agent from a flaky one.

Memory

The model’s context window is finite, so long tasks need a way to store and recall progress — notes, intermediate results, a running plan. Good agents summarise as they go instead of dragging the entire history forward.

Planning

Strong agents break a goal into sub-tasks before diving in, then adapt when steps fail. Weak ones charge ahead and get lost. Much of the recent progress in agents is better planning and self-correction, not bigger models.

What agents do well today

• Coding tasks — writing, editing and debugging code inside a repository, with tests as a feedback signal.
• Research and synthesis — gathering information from many sources and producing a structured summary.
• Structured back-office work — filling forms, reconciling data, moving information between systems with clear rules.
• Customer support triage — handling routine questions and escalating the rest.

These share a trait: the task has clear success signals and bounded steps.

Where they still struggle

The further a task gets from “verifiable in small steps,” the less reliable an agent becomes.

• Long horizons. Errors compound. A 5% chance of a wrong step becomes a near-certain failure over forty steps.
• Ambiguity. Agents fill gaps with assumptions, and those assumptions can be wrong and confident.
• Irreversible actions. Sending money, deleting data, emailing a client — mistakes here are costly, which is why production agents keep a human in the loop for high-stakes steps.
• Brittleness to change. A redesigned website or a renamed button can break a browser agent that worked yesterday.

How to evaluate an agent claim

When a product promises an “autonomous agent,” ask:

• What tools does it have, and what can those tools touch?
• How does it know when it’s done? Vague goals produce vague results.
• What happens when it’s wrong? Is there a check, a rollback, an approval step?
• Has it been tested on real tasks, not cherry-picked demos?

The realistic picture

Agents are genuinely useful where tasks are decomposable and checkable, and oversold where judgement, ambiguity and high stakes dominate. The most effective deployments today are narrow and supervised: an agent doing a specific job with guardrails, not a digital employee running unattended. That gap is closing — but slowly, and step by step.