What Are AI Agents?
Software that decides, acts, and adapts — not just answers.
An AI agent is a system that uses a language model as its reasoning core and pairs it with tools, memory, and a goal. Unlike a chatbot, an agent decides what to do next, takes an action, observes the result, and tries again until it finishes the task or gives up.
Why people care now
Three things changed in 2024 and 2025: models got better at multi-step reasoning, tool calling became standardised, and execution environments (browsers, terminals, IDEs) opened up to model control.
What an agent actually contains
- A model that produces structured output and calls tools.
- A tool layer (HTTP, code execution, file IO, browser, etc.).
- A memory store, often a vector database plus a working scratchpad.
- A controller loop that schedules calls and handles failures.
Where agents work today
Narrow, well-defined tasks: data extraction, research drafts, customer support triage, coding assistants, browser automation. They struggle when the goal is vague, the environment is unstable, or the cost of a wrong action is high.
What to watch
Reliability is the bottleneck, not capability. Most production agents need guardrails: explicit allowlists, retries with cheaper models, human checkpoints, and budgets on tokens and tool calls.
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