What If an AI Agent Was Just a Python Class?

Last Updated on August 19, 2026 by Editorial Team Author(s): Rizwanhoda Originally published on Towards AI. NVIDIA’s new NOOA framework collapses prompts, tools, and state into a single class and it might make you rethink your entire agent stack AI agents have gotten weirdly complicated. After introducing why “simple” agents quickly turn into scattered prompt/tool/state/orchestration systems, the article explains NVIDIA’s NOOA idea: represent an agent as a single Python class where state is modeled via typed fields, prompts live in docstrings, and model-controlled behavior is encoded in specific methods—so capabilities, permissions, and deterministic logic are co-located. It argues this makes state clearer, testing and debugging more natural (especially for deterministic parts), and capability boundaries easier to maintain, while also cautioning that this doesn’t automatically replace graph-based frameworks for complex orchestration needs. It reviews reported benchmark results with caveats, outlines when OO agents are a good fit versus when explicit workflow graphs still win, and closes by reframing the “real question” as how much framework a given agent truly requires—suggesting that many agents might just need an object with an LLM-wired method, not an entire new abstraction layer. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI