Turn AI ideas into real projects

We start from the FastAPI service we deployed to Railway in [the previous workshop](/workshops/end-to-end-agent-deployment), strip out FastAPI, and swap it for a custom AWS Lambda runtime. The runtime handles both the static frontend and the streaming agent API. We deploy one container image as a Lambda Function URL with SSE streaming. Most of the code-writing work is delegated to a coding agent (Codex). The exact prompts I used are quoted verbatim. This was a freestyle session, so it also surfaces a fair amount of meta-discussion: how to work with agents, when to trust them, and when to slow down and read the code. ## Links Related material: - [Previous workshop: end-to-end agent deployment](/workshops/end-to-end-agent-deployment) ## The shift versus the previous workshop The diagram below shows where Lambda fits in the request path: ```mermaid flowchart LR UI["Frontend UI<br/>vanilla JS, SSE"] LAMBDA["Lambda Function URL"] RUNTIME["Custom Lambda runtime<br/>backend/lambda_runtime.py"] AGENT["Agent loop"] SEARCH["FAQ search tool<br/>minsearch"] OPENAI["OpenAI Responses API"] UI -->|GET / and assets| LAMBDA UI -->|POST /ask, /ask/stream| LAMBDA LAMBDA --> RUNTIME RUNTIME --> AGENT AGENT -->|tool call| SEARCH AGENT -->|model call| OPENAI RUNTIME -->|JSON or streamed SSE| LAMBDA ``` The agent loop, the search tool, the renderer abstraction, and the frontend are unchanged from the previous workshop. What changes is the web layer and the deployment pipeline: - FastAPI is gone. A custom Lambda runtime (`backend/lambda_runtime.py`) handles routing, static file serving, and SSE streaming directly against the Lambda Runtime API. - The Dockerfile is rebased on `public.ecr.aws/lambda/python:3.14` instead of `python:3.14-slim`. - Deployment is done with `./deploy.sh`, which builds a container image, pushes it to ECR, and deploys a CloudFormation stack that creates the Lambda function and a Function URL with `RESPONSE_STREAM` invoke mode. - Railway and the GitHub Actions promotion workflow are gone. The benefit of moving to Lambda is the same in plain words: with Railway or Render you pay for a server that has to be up all the time. With a Lambda Function URL you only pay per invocation, which fits tools and small agents that are used occasionally. ## Appendices Two appendices cover material that did not fit into the numbered walkthrough: - [Appendix: file inventory](/workshops/lambda-agent-deployment/tutorial/appendix) - file inventory of the final repo. - [Appendix: isolating the AWS environment](/workshops/lambda-agent-deployment/tutorial/aws-account-isolation) - how to spin up an isolated AWS sub-account, mint short-lived credentials, and ship them to the box that runs the agent. Linked to the reproducible scripts in [aws-account/](https://github.com/AI-Shipping-Labs/workshops/tree/main/2026/2026-05-05-lambda-agent-deployment/aws-account).

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 - [Starting notebook (gist)](https://gist.github.com/alexeygrigorev/8c92913c23ec23e77ce8b355053ac531) ## The app you will build The final app looks like this: ```mermaid flowchart LR UI["Frontend UI<br/>vanilla JS, SSE"] API["FastAPI app"] AGENT["Agent loop"] SEARCH["FAQ search tool<br/>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](https://datatalks.club/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](/workshops/end-to-end-agent-deployment/tutorial/appendix).

Join us for the official launch of AI Shipping Labs! Valeriia and I are launching a new community for AI builders. In this session we'll walk through: - What AI Shipping Labs is and who it's for - The community structure: building sessions, group learning, accountability circles - How the platform was built (almost entirely by AI agents working autonomously) - The tiers and what you get at each level - Live Q&A Whether you're a software engineer moving into AI, an ML engineer who wants to go deeper, or anyone who wants to build and ship AI products — this is for you. Early members get a personal onboarding call to understand your goals and how the community can help.