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End-to-End Agent Deployment

April 21, 2026 Alexey Grigorev

Starting from a Jupyter notebook that runs an agentic-RAG bot, we turn it into a FastAPI service with a vanilla-JS frontend, containerize it, deploy it to Railway, and add a GitHub Actions CI/CD pipeline. Most of the code-writing work is delegated to a coding agent (Claude Code), and the exact prompts I used are quoted verbatim.

Links

The app you will build

The final app looks like this:

flowchart LR UI["Frontend UI vanilla JS, SSE"] API["FastAPI app"] AGENT["Agent loop"] SEARCH["FAQ search tool minsearch"] OPENAI["OpenAI Responses API"] UI -->|POST /ask or /ask/stream| API API --> AGENT AGENT -->|tool call| SEARCH AGENT -->|model call| OPENAI API -->|JSON or SSE| UI

The final app is a minimal teaching-assistant chatbot for the DataTalks.Club Data Engineering Zoomcamp FAQ. One tool is exposed to the model: search(query). Everything else is the web layer, the container, and the deploy pipeline.

Appendix

A file inventory of the final repo is in Appendix: file inventory.

Tutorial pages

  1. Overview and setup
  2. Part 1: The starting notebook
  3. Part 2: Notebook to FastAPI backend
  4. Part 3: Vanilla-JS frontend with Vite
  5. Part 4: Streaming with Server-Sent Events
  6. Part 5: Dockerize as one container
  7. Part 6: Deploy to Railway via CLI
  8. Part 7: GitHub Actions CI/CD
  9. Deferred items
  10. Q&A: side discussions
  11. Appendix: file inventory

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