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All digests tagged Data Governance
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Runlayer secured 300+ agents by deploying a gateway to log every session and tool call. They found risks like prompt injection, data exfiltration, and cost blowups from model switches.
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Bridgewater's Vault processes 100M+ documents, transforming unstructured data into a first-class asset. Learn how they built for both human precision and agent scale using advanced filtering and proactive AI agents.
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Insurance claims are messy, unstructured data nightmares. Learn how AI, combined with robust data pipelines and cross-claim analysis, can transform the industry and solve the biggest data barriers.
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The future of AI engineering involves building robust, governed agent frameworks. Key architectural components include a centralized data vault, a five-layer agent lifecycle, and continuous evaluation loops to manage non-deterministic behavior.
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NASDAQ is adapting its infrastructure and software offerings to meet the demands of high-performance compute and agentic capabilities. The key is building a trusted 'intelligence layer' over existing financial systems.
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AI success is an ecosystem problem, not a model problem. The Full-Stack AI Engineer must orchestrate intelligence across data platforms, agents, and low-code tools, while prioritizing governance and data quality.
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Anders Swanson details how to build a real memory layer for AI agents, moving beyond context windows and scratch files. He outlines a system using multi-model databases, hybrid search, and governance controls for persistent, scalable recall.
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AI News & Strategy Daily | Nate B Jones
Google's $10M bid for old work emails highlights the gap between the performance of work (data records) and the actual value of knowledge work. Understanding this difference is crucial for AI development.
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Stack Overflow details how they used Codex to build two products: a constrained enterprise knowledge layer (Stack Internal) and an agent-first platform (SOFA). Learn about 'archetypes' and designing for agent contribution.
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Learn how to scale AI chatbots from 40 files to 80,000+ documents. This talk covers AI-assisted metadata tagging, sensitive data detection, and maintaining data quality (freshness, conflicts) to ensure reliable RAG context.
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AI News & Strategy Daily | Nate B Jones
AI agents are forcing a re-evaluation of software value. The focus shifts from the application interface to the underlying data, the application workflow, and the agentic layer. Learn how to build for the future.
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AI agents are 'blind,' not stupid. The fix is context engineering at the data layer, requiring federation and logical views to unify data from sources like Kafka and Iceberg.
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Coding agents succeeded because their infrastructure was built for automation. Knowledge work lacks these primitives—Centralization, History, Context, Verification, Governance, and Reversibility—creating a massive gap in reliability.
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Digital Sovereignty is the ability to maintain control over your digital systems, encompassing data, operations, technology, and AI. It requires answering: who controls the data, operations, technology, and AI itself?
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Learn how AI and Data Science combine into a single pipeline. We break down complex concepts like RAG, embeddings, and data cleansing using periodic tables to show how robust enterprise AI applications are built.
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AI products must pass the 'memo test,' requiring verifiable provenance for every claim, reconciling all numbers, and maintaining clear separation between facts and guesses. Accountability requires a human sign-off.
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Experts discuss the $4.99M average cost of a data breach, focusing on how attackers weaponize AI and why basic security hygiene (like access controls) is more critical than ever.
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Paid plans unlock private workspaces with versioning and lineage, SSO (SAML/OIDC), audit logs, and service accounts. Compute is billed separately from storage.
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Learn how RAG solutions fail with complex documents and contradictory laws. We discuss practical techniques like clarification loops and robust document management to handle ambiguity in AI systems.
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