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AI Agents beginner Lesson 1 of 9

AI Agents Learning Roadmap

A structured path from building your first tool-using agent to orchestrating multi-agent systems that plan, remember, and self-correct.

Why AI Agents

Agents extend LLMs from answering questions to completing tasks. A well-built agent can research a topic across 20 sources, write and run code to analyze data, manage files, call APIs, and produce a structured deliverable — autonomously. This roadmap builds the engineering skills to make agents reliable, not just impressive in demos.

Stage 1: Single-Agent Fundamentals (Week 1)

Goal: Build a reliable tool-using agent that handles errors and multi-step tasks.

Topics:

Milestone: Build a research agent with 3 tools (web_search, get_page, summarize). Give it a research question and verify it: calls tools in the right order, handles tool errors without crashing, and produces a cited summary.

Stage 2: Memory and Planning (Weeks 2-3)

Goal: Give agents persistent memory and structured planning capabilities.

Topics:

  • Memory Systems — conversation memory, entity extraction, SQLite KV store, memory retrieval
  • Planning Patterns — plan-then-execute, tree-of-thought, self-correcting agents

Milestone: Build an agent that maintains a persistent knowledge base across conversations. Ask it about a topic in session 1, come back in session 2 and verify it recalls relevant facts and builds on them correctly.

Stage 3: Multi-Agent Systems (Week 3-4)

Goal: Orchestrate multiple specialized agents working together.

Topics:

  • Multi-Agent Systems — orchestrator/worker pattern, role specialization, agent communication

Milestone: Build a 3-agent pipeline: a researcher that gathers information, a critic that identifies gaps, and a writer that synthesizes. Verify the output quality beats a single agent doing all three tasks.

Stage 4: Retrieval-Augmented Agents (Week 4)

Goal: Build agents that retrieve and reason over large knowledge bases.

Topics:

  • RAG Agents — iterative retrieval, multi-hop reasoning, decompose-then-retrieve

Milestone: Build a multi-hop RAG agent that answers questions requiring information from 3+ different documents. Verify it correctly identifies when retrieved context is insufficient and retrieves more.

Stage 5: Evaluation and Autonomous Workflows (Weeks 5-6)

Goal: Measure agent reliability and build production-grade autonomous workflows.

Topics:

Milestone: Build an evaluation harness for any agent: 30 test cases, automated tool-use verification, LLM-as-judge quality scoring, safety test suite. Run it as a GitHub Actions check on every code change.

Stage 6: Projects

Beginner: Calculator agent, SQL query agent, data cleaning agent
Intermediate: Autonomous code debugger, PR reviewer, incident response agent
Advanced: Self-improving agent, multi-agent debate system, long-horizon planning agent

See AI Agents Projects for full descriptions.

What Comes After AI Agents

  • LLMOps — monitoring agents in production, cost tracking, reliability metrics
  • Safety and alignment — red-teaming agents, safe tool use, human oversight patterns
  • Agent frameworks — LangGraph, AutoGen, CrewAI (understand the concepts first, then the frameworks)
  • Specialized agents — coding agents (Devin-like), research agents, data analysis agents

Resources

  • Anthropic documentation on tool use: docs.anthropic.com/tool-use
  • “Building Effective Agents” — Anthropic blog post (essential reading)
  • LangGraph documentation — good for state machine-based agents
  • Academic papers: ReAct, Tree of Thought, Self-Refine

Frequently Asked Questions

What makes an AI agent different from a regular LLM chatbot?
A chatbot responds to a single input. An agent takes actions: it calls tools (search, run code, query databases), observes results, decides what to do next, and loops until the task is done. Agents can complete multi-step tasks autonomously. The challenge is reliability — each step can fail, and the agent must recover gracefully.
Are AI agents ready for production?
For well-scoped, bounded tasks — yes. Agents that search, summarize, classify, or fill structured forms work reliably in production. Open-ended agents with broad tool access and no guardrails still fail in unpredictable ways. The pattern for production: narrow the task scope, add checkpoints requiring human approval for irreversible actions, and monitor everything.