The automated optimization process works by pairing evaluation authoring tools with external coding assistants like Codex or Claude Code to generate tailored tests. These generated benchmarks reproduce real-world errors and measure behavioral compliance using environments such as Harbor or NeMo Gym. Teams use this continuous feedback loop to refine prompts, routing logic, and harness code against both development and held-out test sets. Ultimately, this ensures recurring issues, such as agents sending messages without user consent, are thoroughly validated and resolved.
Key points
Developers utilize NeMo Eval Author with agents like Codex or Claude Code to create targe…: Developers utilize NeMo Eval Author with agents like Codex or Claude Code to create targeted evaluations.
Behavioral compliance is measured across standardized assessment environments, including…: Behavioral compliance is measured across standardized assessment environments, including Harbor and NVIDIA NeMo Gym.
Updated prompts and routing code are validated against held-out tasks to confirm overall…: Updated prompts and routing code are validated against held-out tasks to confirm overall reliability improvements.
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Turn production evidence into prioritized issues and custom evaluations that help you improve prompts, tools, models, memory, skills, and harness code.
When you build an agent for your domain, generic benchmark scores may not explain its failures. An agent might choose the wrong tool, follow the wrong workflow branch, miss relevant context, use an unnecessarily expensive model, or complete a task without meeting a domain-specific requirement.
Improving an agent requires more than model tuning. Prompts, tools, routing, memory, skills, control flow, and other code all shape production reliability. Traces help you investigate failures, while evaluations help you measure whether a change improves agent behavior.
This tutorial walks you through one complete optimization cycle: use NVIDIA NeMo Compass to identify a failure in recorded traces, create an evaluation with NVIDIA NeMo Eval Author, update the agent’s instructions, and compare results on development and held-out tasks. Follow along with the NVIDIA NemoClaw blueprint for agent optimization.
The blueprint uses Hermes Agent in an NVIDIA OpenShell sandbox, with recorded traces from a fictional company.
The social media giant is bringing specialized workflow technology into its core artificial intelligence division to boost developer capabilities. By absorbing an existing platform designed for simultaneous agent orchestration, the company reinforces its internal resources targeted at advanced automation systems. The move directly integrates specialized engineering talent into the company's frontier research initiatives.
Tech companies face mounting pushback from instructors over how deeply automated features are woven into standard classroom tools. By packaging search functionality strictly with automated summaries, platform providers risk eroding the independent source evaluation and research friction essential to student literacy. School leaders are demanding autonomy to dictate when automated tools enter the curriculum rather than accepting bundled vendor mandates.
Consumer technology firm Nothing is preparing to introduce its inaugural artificial intelligence hardware, marking a major milestone in its long-term product roadmap. The rollout delivers on previous commitments by leadership to integrate dedicated intelligence systems into physical consumer electronics. This development signifies the brand's formal entry into the emerging market for specialized, standalone automated gadgets.