NVIDIA Labs has unveiled NOOA (Object-Oriented Agents), an open-source research framework that redefines AI agent development by treating the underlying architecture as a standard Python object. Moving beyond traditional prompt orchestration and fragmented tool schemas, the framework asserts that harness design dictates agent performance as critically as the foundation model itself. NOOA structures agents as single Python classes where methods represent capabilities, fields manage state, type annotations enforce contracts, and docstrings serve as prompts. This design enables standard software engineering practices, including version control, unit testing, and code review, while significantly reducing development complexity. The architecture is built on six core capabilities designed to maximize model efficiency and accuracy. These include typed input and output validation, pass-by-reference execution that allows models to interact with live Python objects rather than serialized text, code-as-action workflows where the model writes and executes Python directly, programmable orchestration loops, explicit durable state management, and model-callable harness APIs for context inspection. A dedicated memory subsystem complements these features by enabling agents to curate long-term knowledge through typed, relationship-mapped records rather than automatic summarization pipelines. This approach allows systems to accumulate insights across sessions without retraining, persisting all data in a transparent SQLite database. Independent benchmark evaluations demonstrate substantial performance and efficiency gains. On the SWE-bench Verified dataset, NOOA achieved an 82.2 percent accuracy rate using GPT-5.5, surpassing published state-of-the-art results. Crucially, it accomplished this while consuming approximately 1.1 million tokens per task, roughly half the cost of competing harnesses that require over two million tokens to achieve lower scores. The pass-by-reference mechanism eliminates the need for context compaction, maintaining append-only transcripts and maximizing prefill cache efficiency. In cybersecurity evaluations on CyberGym L1, the framework solved 86.8 percent of vulnerability rediscovery tasks using commodity models operating in fully offline, sandboxed environments. The system enforces deterministic verification gates, ensuring crash proofs are validated through strict code execution rather than flexible prompt instructions. Similarly, on the ARC-AGI-3 reasoning benchmark, NOOA scored 50.2 percent RHAE with GPT-5.5 and reached 85.1 percent with GPT-5.6-sol, advancing the benchmark score-cost Pareto frontier at under twenty dollars per game. Security and reproducibility remain central to the release. The framework incorporates layered sandboxing, privilege drops, and comprehensive red-team audits to prevent context leakage during complex reasoning and execution tasks. By publishing the complete framework, evaluation methodology, and benchmark agents, NVIDIA provides a transparent, auditable foundation for the AI agent ecosystem. The project is positioned as a modular research surface, enabling developers to adopt its object-oriented principles, challenge its benchmarks, and integrate its efficiency gains into existing production environments.