7 items under this folder.
AI agent evaluation becomes operationally useful when it identifies where a workflow failed: planning, tool selection, argument construction, execution, or final output. CTOs and AI leaders should combine deterministic checks, rubric-based model judges, repeated trials, regression suites, and production traces to establish release evidence rather than relying on demonstrations or single-run accuracy.
Headless tools can close the operational gap between server-hosted agents and the client applications where users actually work. The pattern is adoption-ready for bounded capabilities with explicit permissions, typed schemas, and human approval, but it is not evidence that arbitrary client automation is secure or reliable by default.
Mistral AI Workflows addresses the operational gap between demonstrating an AI agent and running a dependable enterprise process. Its public-preview architecture combines Python-defined workflows, stateful recovery, approval checkpoints, tracing, and customer-hosted execution workers, but production readiness still depends on model reliability, rollback design, ownership, and infrastructure validation.
Use this note to assess whether rubric-guided self-correction can improve the reliability of bounded enterprise agent workflows without treating model-based grading as proof of correctness.
This note is a Quartz-ready operating map for the Claude Agent SDK. It explains the SDK as an agent runtime rather than a simple prompt wrapper.
This note is a Quartz-ready operating guide for using Claude Cowork or Claude Code without losing cost control, context quality, or review discipline.
This note is a Quartz-ready onboarding map for Claude Cowork, focused on delegation, projects, connectors, skills, plugins, browser use, and review discipline.