Overview
An AI agent is a system built on a model that can pursue a goal over multiple steps — breaking a task down, using tools (search, code, browsers, other software), observing the results, and adjusting — rather than producing a single reply. The shift from answering questions to completing tasks is the defining development of AI in 2025–26.
Agents turn a language model into something closer to a digital worker. They also move the hard problems from raw cognition to reliability, oversight, and security — an agent that can act can also act wrongly, or be manipulated.
How it works
Plan
The agent breaks a goal into steps and decides what to do first.
Act with tools
It calls tools — searching, running code, browsing, editing files — to make progress.
Observe
It reads the results of its actions and updates its understanding.
Loop
It repeats plan–act–observe until the task is done, ideally with human checkpoints.
A concrete example
Asked to 'research three suppliers and draft a comparison', an agent searches for each, reads their pages, extracts the relevant details, and assembles a document — chaining tool calls that a plain chatbot couldn't. The same autonomy is why oversight and guardrails matter so much.
Limits & risks
- Errors compound over multi-step tasks — a wrong early step derails everything after.
- Security risks like prompt injection, where malicious content hijacks the agent.
- Autonomy without oversight can take costly or irreversible actions.
Frequently asked questions
How is an agent different from a chatbot?
A chatbot answers; an agent pursues a goal over many steps, using tools and reacting to results — it does work rather than just responding.
What's the biggest challenge with agents?
Reliability and safety — because agents act autonomously, errors compound and they can be manipulated, so oversight and guardrails are essential.