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All digests tagged AI Observability
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CoreWeave Agent Lens delivers intelligent agent observability at enterprise scale, providing a single view of every trace to turn production issues into proven fixes.
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Jev provides structured decisions and confidence probabilities, but the speaker warns that confidence is not evidence. For trustworthy AI, robust observability and audit trails are essential to verify decisions before trusting them.
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Deploying AI in major banks requires overcoming compliance hurdles. The speaker details three pillars: securing the build pipeline, ensuring disaster recovery, and preventing PII leakage using advanced techniques like agent-based CVE testing.
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Evals are running tests on traces. They are essential because agents are non-deterministic, meaning the same prompt can give different answers. This process allows teams to measure reliability and prove that changes actually improve the agent.
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MasterClass details its multi-agent AI system, NCX, and the advanced observability stack built on CoreWeave. Learn how tracing, MCP servers, and an AgentOps loop enable self-improvement in complex AI applications.
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This workshop details how to master AI Observability by building a continuous agent improvement flywheel. Using Braintrust, engineers can derive signal from production traces via custom scoring and Topics, automating the process of identifying failures and proposing code changes.
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To manage AI spend, tag every resource and move beyond total cost. Focus on efficiency metrics like 'cost per thought,' 'reasoning ratio,' and 'cache hit rate' to truly understand AI ROI.
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MLflow provides deep observability for multi-agent systems, moving beyond simple HTTP monitoring to trace internal LLM calls, tool usage, and complex failures like non-determinism. Learn how to use LLM judges and integrate tracing into CI.
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