Building an AI Agent with Ruby and Rails from Scratch — What No One Tells You
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
Building an AI agent with Ruby on Rails involves more complexity than simple workflow automation. Key challenges include implementing MCP (Model Context Protocol) servers for tool calling, managing context window limitations, choosing LLMs that support tool calls, and handling the orchestration loop between the LLM and tools.
Table of contents
Building an AI Agent with Ruby and Rails from Scratch — What No One Tells You0. TL;DR💡 1. From Workflow to Agent — Easy in Theory, Right?🛠 2. Enter MCP: Testing Tools with Curl (Because Why Not?)📏 3. Context Windows: The LLM’s Mortal Enemy🤖 4. Choosing the Right LLM: Because Not All Models Are Created Equal🪤 5. Ruby SDK Gotchas: The Fine Print They Don’t Bold in the Docs📚 6. How Does the LLM Even Know What to Do?🗂 7. Rails Structure: Order in the Chaos🔌 8. Talking to the LLM (and a Tiny MCP Client)🔄 9. The LLM Client Shuffle: OpenAI, LM Studio, or Ollama?11. 💬 Talking to the LLM12. 📥 The Response13. 🪄 Dirty Tricks14. ⏱️ Fast-Moving Target (a.k.a. “The Docs Today, The Code Tomorrow”)Sort: