AI Agents: Introduction
A plain LLM answers one prompt at a time. An AI agent goes further: it pursues a goal on its own — planning steps, using tools, keeping memory, and reasoning through problems in a loop until the task is done. Agents turn a language model from a text generator into a system that gets things done.
💡 In one line: An AI agent uses an LLM as its brain to autonomously plan, remember, reason, and use tools to accomplish a goal.
What is an AI Agent?
An AI agent is an LLM-powered system that autonomously pursues goals: it decides what actions to take, uses tools, observes the results, and iterates until it's done. The LLM is the "brain," and around it sit four capabilities — planning, memory, reasoning, and tools — plus a control loop.
Agent vs. Plain LLM
| Plain LLM / chatbot | AI agent | |
|---|---|---|
| Interaction | One prompt → one response | Goal → many steps |
| Actions | None | Uses tools / takes actions |
| Memory | Just the context window | Short- and long-term memory |
| Control | You drive each step | Decides its own steps |
An agent is goal-directed and autonomous; a chatbot just responds.
Core Components
These four pillars are the rest of this topic:
- Planning — break the goal into an ordered set of steps.
- Memory — retain context and history (short-term and long-term).
- Reasoning — think through the problem and decide the next action.
- Tools — call external functions and APIs to act and fetch information.
Together with the LLM core and the agent loop, they make autonomy possible.
The Agent Loop
Agents run a perceive → reason → act → observe cycle until the goal is met.
This is the ReAct-style loop you saw in prompt engineering — now as the engine of an agent.
Examples & Use Cases
- Coding agents — read, write, run, and debug code.
- Research assistants — search, read, and synthesise.
- Task automation — book, file, update, notify.
- Customer support and multi-agent systems (agents that collaborate).
Levels of Autonomy
Agents span a spectrum:
- Tool-augmented — a single tool call within a response.
- Multi-step agent — plans and executes several steps.
- Autonomous / multi-agent — long-running, self-directed, or teams of agents.
Benefits
- Handle complex, multi-step tasks a single prompt can't.
- Use live tools and data, not just training knowledge.
- Adapt based on what they observe.
Challenges
- Reliability — agents can go off-track or loop forever.
- Hallucination, cost/latency (many LLM calls), and safety.
- Hard to evaluate — success is multi-step and open-ended.
What's Ahead
The next subtopics dig into each pillar: Planning, Memory, Reasoning, and Tools.
Summary
- An AI agent uses an LLM as a brain to autonomously pursue goals.
- Unlike a chatbot, it plans, remembers, reasons, and uses tools over many steps.
- It runs a loop: reason → act → observe → repeat → answer.
- Agents unlock complex, tool-using, adaptive tasks — with challenges in reliability, cost, and safety.
- Its four pillars — Planning, Memory, Reasoning, Tools — are covered next. EOF echo created