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Tracing Agent Harness Behavior with NVIDIA NeMo Relay

Illustration of a developer at code screens, with a camera and Nous logo beside task success panels showing 70% and 81%.An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching...Illustration of a developer at code screens, with a camera and Nous logo beside task success panels showing 70% and 81%.

An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching the same content again. A correct final answer hides those extra steps, even though they increase latency and consume tokens. Inefficiencies create more chances for failure. To improve an agent’s behavior…

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Add a Specialized Deep Research Skill to Agent Harnesses

The image depicts various digital screens showing concepts related to a "Skills Repository," "Software Architecture," "Big Data Schema," and "Training New Sub-Agent," suggesting a theme of self-evolving artificial intelligence capabilities.Agent harnesses like Claude Code, Codex, and LangChain Deep Agents are excellent orchestrators. They manage sessions, chain tools, execute code, and respond to...The image depicts various digital screens showing concepts related to a

Agent harnesses like Claude Code, Codex, and LangChain Deep Agents are excellent orchestrators. They manage sessions, chain tools, execute code, and respond to developer intent. But when these harnesses need to do deep research, such as multi-document synthesis, decision briefs backed by enterprise data, and long-horizon analysis with source attribution, the complexity of deep research shifts back…

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