AI Agents: What They Are and Why Everyone's Suddenly Talking About Them

Sofia: The word "agent" is everywhere in AI right now. What does it actually mean?

Sofia: An agent, in the AI context, is a system that can perceive an environment, reason about it, take actions, and observe the results. The key word is "actions." A standard LLM responds to you. An agent does things — it might browse a website to check a fact, write and execute code to analyse data, or send an email on your behalf. The output isn't just words; it's effects in the world.

Sofia: Why is this such a big deal compared to, say, ChatGPT?

Sofia: The difference is autonomy and consequence. When ChatGPT gets something wrong, you read wrong information. When an agent gets something wrong and it's booking your flights or executing a trade or sending emails to your clients, the consequences are real and sometimes irreversible. That changes the risk calculation completely.

Sofia: So what safeguards exist?

Sofia: The field is still developing norms. The most common safeguard is the "human in the loop" — requiring human approval before any irreversible action. Some systems use a confidence threshold: high-confidence actions execute automatically, low-confidence ones pause for review. The most sophisticated approach is constitutional AI — building values and constraints into the model itself, not just the application layer. But none of these are solved problems. We're in the earliest stages of understanding how to deploy agentic AI safely.