Build a Production-Ready YouTube AI Agent with Temporal
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We build a deep research agent over the DataTalks.Club podcast archive. We start by downloading and indexing YouTube transcripts. Then we turn that ingestion code into a durable Temporal workflow. Once the data is searchable, we build a Pydantic AI research agent. We add a summarization sub-agent for long transcripts and wrap the agent run in Temporal too.
Links
The main resources for this workshop:
The system you will build
The final system looks like this:
The ingestion side has the parts that usually fail in production. It makes network calls, runs proxies around YouTube's cloud-IP blocks, and writes to Elasticsearch. It also loops over many videos. Temporal gives that side retries, observability, and durable execution.
The agent side uses the indexed data to answer questions from the podcast archive. It uses Temporal again too, so long agent runs can survive failures.
Hosted by
Alexey Grigorev
Chief Agent Officer at AI Shipping Labs
Software engineer and machine learning practitioner with 15+ years of experience building production ML systems. I focus on practical, production-grade ML and AI systems, from early prototypes to reliable systems in production.
I'm the founder of DataTalks.Club, a free community that connects tens of thousands of practitioners worldwide, and the creator of the Zoomcamp series, free, code-first programs that have reached 100,000+ learners globally.
At AI Shipping Labs, I'm building the kind of environment that would have accelerated my own career growth. After years of teaching at scale, I wanted something more focused: a space for action-oriented builders who want to turn AI ideas into real projects. The community gives members the structure, accountability, and peer support to ship practical AI products consistently, even alongside their main jobs.
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