Full-Stack AI Engineering: Building an AI Support Agent with FastAPI and Docker

VideoCURTTutorial

In this comprehensive tutorial, Dave from Datalumina demonstrates how to build an end-to-end AI customer support agent. The project covers backend architecture, Gmail integration, evals, and Docker deployment.
Playing loads YouTube (youtube-nocookie.com).

Key points

  1. Connecting a Gmail inbox using the Gmail API and a custom poller
  2. Building backend services and the AI pipeline with FastAPI, Pydantic AI, and Claude
  3. Managing data and knowledge base integration using Supabase
  4. Evaluating agent output quality and building a React admin dashboard
  5. Deploying the containerised full-stack application with Docker on a VPS

Why it matters

This walkthrough demonstrates how to transition AI prototypes into real-world, production-ready systems combining agent frameworks, testing, and modern deployment practices.

Analysis

This course covers the end-to-end implementation of an automated customer support system designed as a portfolio-grade project.

Core Architecture

  • Ingestion & Messaging: Inbound and outbound handling through the Gmail API.
  • Agent Pipeline: Backend built with FastAPI, utilising Pydantic AI and Claude alongside a dedicated knowledge base.
  • Data & UI: Storing agent activity in Supabase paired with an administrative interface built in React.
  • Testing & Deployment: Implementation of evaluations (evals) to measure output quality, followed by containerised deployment via Docker.

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Summary generated by AI (Gemini 3.8 Flash, 1 November 2026). It may contain errors – the original source is authoritative.

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