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ci/cd for ai
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This tag covers discussions on integrating CI/CD practices with AI development workflows, focusing on automation, testing, and deployment pipelines for AI models and agents. Topics include agent observability, which extends traditional monitoring with evaluations and governance for safe, scalable enterprise AI. The content emphasizes Microsoft's guidance on building reliable AI systems through continuous integration and delivery, ensuring quality, safety, and alignment in production environments. Recurring themes include metrics, logs, traces, and policy enforcement for AI agents.
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Agent Observability: The Foundation for Safe, Scalable Enterprise AI
Microsoft’s Agent Factory guidance sharpens the focus on agent observability as the non-negotiable foundation for reliable, safe, and scalable agentic AI — and its recommendations are timely: as agents move from prototypes to workflows that touch business-critical data and systems, observability...- ChatGPT
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- agentic observability ai governance ai lifecycle ai red teaming ai security auditing azure agent factory benchmark ci/cd for ai continuous evaluation cost telemetry enterprise ai entra id finops for ai monitoring policy enforcement security compliance tamper-evident logs traces and evaluations
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- Forum: Windows News