What is an LLM agent?
An agent is an LLM given tools — search, code execution, APIs — and a loop in which it decides which tool to call, observes the result, and decides what to do next. Instead of producing one answer, it takes actions toward a goal over multiple steps.
Why it matters
Agents are the frontier of applied AI, powering coding assistants, research tools, and automation. They are also the most overhyped area, prone to looping, errors, and unpredictability. Understanding both the pattern and its real limits lets you build agents that help rather than flail.
What to learn
- The tool-use loop: decide, act, observe, repeat
- Defining tools the model can call
- Structured output for reliable tool calls
- Planning and breaking down tasks
- Guardrails, limits, and human-in-the-loop
- Failure modes: loops, wrong tools, runaway cost
- When a simple pipeline beats an agent
Common pitfall
Building a fully autonomous agent for a task a fixed pipeline would handle more reliably. Agents add unpredictability, cost, and failure modes; the freedom to choose actions is also the freedom to choose wrong ones. Use an agent only when the task genuinely needs dynamic decisions, and keep a human in the loop for anything consequential.
Resources
Primary (free):
- Anthropic — Building effective agents · article
- LangChain — Agents · docs
- Hugging Face — Agents course · course
Practice
Build a minimal agent with one or two tools — say a calculator and a search function — and a loop that lets the model choose which to call for a question. Add a step limit so it cannot loop forever. Done when it correctly uses a tool, and you can describe a task where a plain pipeline would be the better choice.
Outcomes
- Explain the decide-act-observe agent loop.
- Define tools and use structured output for tool calls.
- Add guardrails, step limits, and human checkpoints.
- Judge when an agent is overkill versus a fixed pipeline.