Prompt Engineering Tutorial 0/120 lessons ~6 min read Lesson 102
AI Agents
AI agents combine planning + tool use + memory + reflection to accomplish multi-step goals.
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Focus
6 guided sections
Practice signal
Examples included
Career prep
Foundation builder
Introduction
AI agents combine planning + tool use + memory + reflection to accomplish multi-step goals. Their prompts are the system's brain.
Beginner analogy: Like an intern with tools — needs a clear goal + tool descriptions + judgement.
Understanding the topic
Core concepts to understand:
- Planner prompt + tool descriptions + memory.
- Loop: plan → act → observe → reflect.
- Caps on steps, cost, tools.
- Human approval for high-stakes actions.
- Frameworks: LangGraph, AutoGen, CrewAI.
Syntax reference
Visual workflow / architecture:
bash
Goal → Plan → Tool → Observe → Plan → ... → Done
Real-world use
Devin, OpenAI Operator, Cursor Agent — all production AI agents.
Best practices
- Cap everything (steps, cost, tools).
- Sandbox tool execution.
- Human-in-loop on side effects.
Hands-on exercise
Interview preparation — practice these questions:
- Q1. Agent prompt architecture?
- Q2. Caps — why?
- Q3. Sandbox tools?
- Q4. Human approval points?
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