Six Agent Harness Capabilities for Higher Model Performance | NVIDIA Technical Blog

What this post added

This post introduces the NVIDIA Labs Object-Oriented Agents (NOOA) framework, an open-source research preview designed to improve agent performance. NOOA structures agents as single Python classes, treating methods as capabilities, fields as state, and docstrings as prompts. It leverages type annotations as enforced contracts and uses an LLM-driven loop to complete method bodies marked by ellipses at runtime. The framework implements six key model-facing interface ideas: typed input/output, pass by reference, code as action, programmable loop engineering, explicit object state, and model-callable harness APIs. A novel long-term memory subsystem is also introduced, allowing agents to curate typed, relational memory in a human-readable SQLite store, supporting knowledge accumulation and efficient context management through pass-by-reference, eliminating the need for context compaction or summarization pipelines. Benchmarks on SWE-bench Verified, CyberGym L1, and ARC-AGI-3 demonstrate state-of-the-art performance and efficiency, achieving higher accuracy and lower token costs compared to prior harnesses, with reproducible, inspectable workflows and open evaluation methodology.