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Digests from IBM Technology
AI is exposing sensitive data through complex systems like RAG pipelines and agents. Traditional DLP is insufficient; organizations need AI-aware platforms to track data lineage across both workload and workforce streams.
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Explore the frontier of AI efficiency: From Claude Opus 5.5 and GPT-6 Sol to TypeSafe's Jev AI, learn how structured, calibrated models are revolutionizing automation and specialized scientific computing.
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Learn how AI Agents combine specialized APIs and LLMs to analyze complex, real-time data, using the US Open tennis serve as a case study. This pattern divides labor between computation and reasoning.
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AI agents are not the revolution; they are the catalyst. The real impact is accelerating foundational changes in data, systems, and connectivity, forcing standardization and interoperability.
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AI success is shifting from token consumption to Valuemaxxing. Teams must measure outcomes—like deployments completed or rework avoided—rather than just AI usage volume.
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Super agents offer centralized intelligence but carry risks of privilege abuse and expanded attack surfaces. Secure design requires agent swarms, least agency, isolation of tools, and observability.
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The discussion covers the debate around slowing AI development (pacing), details the technical features of IBM's Granite 4.2 models, and analyzes Meta's new personal agent, Muse.
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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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The discussion emphasizes shifting focus from patching vulnerabilities to validation, while also addressing the convergence of AI threats and PQC migration for financial institutions.
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Learn how AI is accelerating the modernization of mission-critical legacy systems, translating old code (Cobol, Mainframe) into modern, scalable architectures.
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AI accelerates code generation, but security requires a 'shift-left' approach. Learn five key principles for securing AI-assisted development, focusing on outcome validation and dependency integrity.
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To become an AI Engineer, focus on a three-tiered skill stack: Python, APIs, RAG, Agents, and deployment tools like Kubernetes. The role is building systems around existing LLMs.
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Explore the latest in AI: From GPT-6 Astra solving the Navier-Stokes problem to IBM's AI-powered US Open analytics and the dangers of the WeWorm exploit.
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Experts discuss why controlling AI agents is difficult due to their probabilistic nature, requiring hard deterministic controls at runtime. The segment also covers OWASP Top 10 for agentic skills and the use of ThreatXtension.
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Learn six essential AI concepts—from LLMs to RAG and Agents—using simple human analogies. Understand how modern AI systems work by connecting the 'brain' to its tools.
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Anthropic's Model Hardware Standard (MHS) is redefining AI interaction with physical equipment. We analyze the risks of agent escapes and the determinism challenges posed by World Models.
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Experts discuss OpenAI's call for a global cyber defense surge, focusing on shifting from threat intelligence to rapid remediation strategies and the necessity of human oversight in autonomous AI agents.
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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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AI is shifting the focus of software reviews from line-by-line implementation details to validating business outcomes, requirements, and overall intent. This marks a major evolution in development workflows.
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In order to get reliable, uninterrupted access to the public web data on the internet, the knowledge layer needs to get the information from specialized tools. This is where the web data infrastructure layer comes into play.
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