Container-enabled Asyncio is All You Need (to Build Pythonic AI Workflows at Scale) - Niels Bantlin

From the creator

As AI applications and agents move from prototypes to production, Python developers are increasingly tasked with orchestrating large numbers of models, tools and external services. These requirements often push teams toward specialized frameworks or domain-specific languages to manage concurrency and workflows, even though Python’s standard library already provides the core building blocks to solve these problems. This talk demonstrates how engineers can leverage Python’s native asyncio library together with container orchestration platforms like Kubernetes to build scalable, production-ready AI workflows. It presents a practical explainer of asyncio, emphasizing the aspects most relevant to today’s AI systems, such as structured concurrency, task coordination, backpressure, timeouts and failure isolation. It demonstrates how asyncio can be a highly effective programming paradigm to coordinate compute and data flow on a Kubernetes backend, giving Python developers the scale they need to build production-grade AI applications and agents. Through concrete examples, the session shows how common workflow patterns, including coordinating LLM calls, executing tools in parallel, streaming responses and interacting with rate-limited APIs, can be implemented directly with asyncio and other Python primitives. Rather than relying on declarative pipelines or custom Domain-Specific Language (DSLs), these patterns remain explicit, debuggable and easy to reason about using plain Python.

Published 2026-07-02 · Watch on YouTube ↗
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