// Stage 8 · RAG, Agents & AI Engineering
Agents: Tool Use, MCP, Planning & Memory
ReAct, function calling, MCP, reflection, memory, multi-agent systems, computer use and sandboxes.
about 12 hours10 algorithms4 paperslesson text ready
01Goals
- Implement a ReAct loop
- Write an MCP server
- Design an agent’s memory and failure recovery
Do these first: RAG Engineering & Evaluation · Prompting & Using AI Well
02Read on this site
03Algorithms to master
Each card has the original paper, prerequisites and resources.
04Must-read papers
- ReAct: Synergizing Reasoning and Acting in Language Models ↗2022 ✓ verified in Essentials
- Toolformer: Language Models Can Teach Themselves to Use Tools ↗2023 ✓ verified in Essentials
- Reflexion: Language Agents with Verbal Reinforcement Learning ↗2023 ✓ verified in Essentials
- SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering ↗2024 ✓ verified in Essentials
05Courses & videos
- huggingface.co ↗CoursesEN
- huggingface.co ↗CoursesEN
- Anthropic:Building effective agents ↗ExplainersEN
- Lilian Weng:LLM Powered Autonomous Agents ↗ExplainersEN
- langchain-ai.github.io ↗Official docsEN
06Hands-on: project, code & exercises
Free, official or author-published pages. Most run in the browser or on a free Colab GPU.
PROJECT
Write an MCP server that can check the weather, read local files and run web searches; connect it to any MCP client and complete 3 multi-step tasks.
- 1
- 2modelcontextprotocol.io ↗Official docs
Write an MCP server with the official tutorial and connect it to a client.
- 3
07Self-test
Answer in your head, then open to check.
What three parts make up a ReAct loop?
Thought, action, observation.
What message protocol is MCP built on?
JSON-RPC 2.0.
SWE-agent’s key finding?
Designing a dedicated agent–computer interface (ACI) for the model greatly improves results.
On these routes: Everyday Users · Application Developers · Product Managers & Leaders · Domain Scientists