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While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.

The AI-Q skill enables Claude Code, Codex, or other general-purpose agents to submit a research task to a running AI-Q server and receive a well-formatted, detailed report with citations. The skill includes a SKILL.md file that tells the harness how to use AI-Q, plus a helper script that manages request routing, job submission, polling, and result retrieval. A skill can mean different things in agent workflows. Agent skills guide the harness, the NVIDIA NeMo Agent Toolkit helps define reusable tool functions, and the AI-Q Agent Skill exposes the full research pipeline—including intent classifi

AGENTS.md files help Codex and similar AI tools understand project-specific instructions, coding conventions, and organizational structures. They can reside anywhere within a Codex container, providing valuable context to AI agents. Like other project configuration files, these instructions are treated as trusted context by the agent. This trust model is by design, but it creates an interesting attack surface when a malicious dependency is able to write or modify these files at build time.

This attack path highlights important considerations for the future of agent-assisted development. Extended supply chain risk: Traditional supply chain attacks focus on injecting malicious code directly. In agentic environments, a compromised dependency can also redirect the agent itself, extending familiar supply chain risks into a new dimension, such as injecting subtle delays that cause performance degradation or denial-of-service scenarios.   Instruction following under adversarial conditions: When the agent followed injected configuration directives, including instructions to conceal its