enterprise data management

Google researchers introduce 'faithful uncertainty,' allowing LLMs to offer best guesses instead of hallucinations

Large language models (LLMs) continue to grapple with hallucinations, a significant hurdle for enterprise adoption. Google researchers have introduced "faithful uncertainty," a technique that allows models to express their confidence—or lack thereof—in responses. This metacognitive approach moves beyond the binary "answer-or-abstain" and enables models to offer appropriately hedged hypotheses, like "My best guess is..."—a crucial control layer for agentic AI.

Best Data Analytics Courses in 2026

Finding the best data analytics course in 2026 requires navigating a diverse landscape of tools, roles, and learning objectives. This guide reviews ten leading courses, ranging from foundational certificates to immersive, project-based programs and even free official training for platforms like Tableau and Power BI. We’ve prioritized options that empower users to transform their data skills and achieve tangible results. For a broader perspective on incorporating user insights, explore our related article, "The Benefits Of Cognitive Inclusion In UX Research."

What AI benchmarks miss about real-world performance

Enterprise AI teams are optimizing for compute, often overlooking a critical bottleneck: the data path between storage and processing. Standard benchmarks fail to replicate real-world conditions—latency spikes and network instability—that significantly degrade AI performance. F5 and MinIO testing revealed that even modest latency dramatically impacts S3 throughput, highlighting the need for a more resilient approach. F5’s ADSP acts as a vital control point, ensuring data delivery and maximizing GPU utilization, as demonstrated by SecureIQLab's validation.

Why AI that works in the lab often fails in production — and what actually fixes it

Many enterprises struggle to translate promising AI prototypes into reliable, production-ready systems. At Capital One, we’ve observed that successful AI implementation demands a disciplined research and development approach, connecting foundational work to real-world applications and rigorously evaluating progress. Bridging the gap between research and practical use—as demonstrated by our work with multi-agent architectures—is key to unlocking impactful AI solutions. Learn how organizations can transform AI ambition into production reality through deliberate research, evaluation, and deployment.

Microsoft’s open-source SkillOpt automatically upgrades AI agent skills without touching model weights

Microsoft’s new, open-source framework, SkillOpt, streamlines the optimization of AI agent skills—a crucial element for real-world AI applications. Traditionally, refining these skills, which are sets of instructions guiding models, requires tedious manual adjustments. SkillOpt introduces an optimizer that treats these skill documents as trainable objects, evolving them based on performance feedback using deep-learning techniques. Initial results, demonstrated on models like GPT-5.5 and Qwen, show SkillOpt significantly boosts accuracy and delivers compact, transferable skill artifacts, addressing a key challenge in agentic AI.

Xiaomi's new open source, agentic AI coding harness MiMo Code beats Claude Code at ultra-long, 200+ step tasks

Xiaomi has open-sourced MiMo Code V0.1.0, a terminal-native AI coding assistant that demonstrates impressive performance, outperforming Anthropic's Claude Code on long-horizon coding tasks. This innovative harness, built on the OpenCode agent, incorporates a unique cross-session memory system designed to overcome AI coding agents’ tendency to "forget" earlier instructions. Developers can explore the tool immediately with limited-time free access to Xiaomi’s powerful multimodal MiMo-V2.5 model, requiring no registration. For those seeking deeper insights into AI agent skill optimization, explore our related article on Microsoft's SkillOpt.

Best Data Engineering Courses in 2026

Best Data Science Programs in 2026

In 2026, navigating data science education feels like sprinting through a maze of degrees, bootcamps, and online courses, each claiming the quickest route to a career. The market ranges from free YouTube tutorials to multi‑hundred‑thousand‑dollar master’s programs, yet many comparison lists flatten these options without clarifying which path best fits your goals. This guide ranks programs by curriculum depth, industry relevance, and return on investment, helping you choose a course that truly transforms your data skills and accelerates your career.

Researchers trained an open source AI search agent, Harness-1, that outperforms GPT-5.4 on recalling relevant information

Researchers from UIUC, UC Berkeley, and the open‑source vector database Chroma have unveiled Harness‑1, a 20‑billion‑parameter AI search agent that outperforms GPT‑5.4 on information recall, achieving a 73 % average score across eight complex benchmarks. Built on OpenAI’s gpt‑oss‑20B model and released under Apache 2.0, Harness‑1 demonstrates that a well‑designed external “harness” can replace brute‑force context scaling, delivering enterprise‑grade accuracy with lower compute costs.

Claude Mythos exposed a hard truth: Your enterprise patching process is way too slow

In 2024, research revealed a stark reality: enterprise patching processes are lagging as AI capabilities advance. With Claude Mythos now autonomously discovering thousands of zero-day vulnerabilities, organizations find themselves in a race against time. The average exploitation timeline is shrinking dramatically, challenging traditional patching assumptions. To combat this, it's essential to adopt a three-layer prioritization filter and implement event-driven patching. For deeper insights on the evolving landscape of AI in security, explore our article on "TechCrunch Mobility," highlighting the intersection of technology and innovation.

The AI agent bottleneck isn't model performance — it's permissions

The challenge facing enterprise AI agents isn't their performance, but rather the complexities of permissioning. As workflows encounter limits on what agents can access and manage, Workday addresses this by integrating its existing system of record as the governance layer for AI agents. Gerrit Kazmaier, Workday’s president for product and technology, emphasizes the importance of maintaining a robust security model to avoid pitfalls in DIY AI solutions.

MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%

MeMo's innovative memory model enables teams to enhance their large language models (LLMs) without the need for costly retraining, achieving a notable 26% performance increase. By addressing the challenges of static knowledge in enterprise AI, MeMo employs a modular architecture that separates knowledge encoding from reasoning, making it adaptable to both open-source and proprietary models. This efficient approach allows for continuous updates with minimal risk of catastrophic forgetting.

Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Researchers from Meta, Google, and several universities have introduced AutoTTS, a groundbreaking framework that automates the design of test-time scaling (TTS) strategies for large language models. By eliminating the manual bottleneck historically tied to human intuition, AutoTTS enables organizations to dynamically optimize compute allocation, significantly reducing token usage by up to 69.5% without compromising accuracy. This innovation not only streamlines operational costs but also enhances peak performance in real-world applications.

SQL query logs hold the context AI agents need to stop hallucinating joins

SQL query logs are crucial for AI agents to avoid misinterpreting data joins, as demonstrated by Miro's experience with over 10,000 tables in Snowflake, where inaccuracies arose more than 65% of the time. The challenge was rooted in the lack of contextual understanding. DataHub is addressing this with its upcoming Context Intelligence layer, which leverages SQL query history to create a semantic index, guiding agents toward validated data connections. This innovative approach empowers organizations to transform their data management practices, making AI-driven insights more reliable.

How DeepSeek’s radical architecture is shattering Silicon Valley's token moat

DeepSeek’s recent announcement of a permanent 75% price cut on its V4 Pro model marks a significant disruption in Silicon Valley’s AI landscape, challenging capital-intensive business models. By offering a solution that is 7x cheaper on inputs and 17x cheaper on outputs compared to leading competitors, DeepSeek not only enhances affordability but also promotes efficiency through innovative hardware-software architecture.

Control within connection: How data sovereignty is rewriting the rules of critical infrastructure

In a rapidly evolving digital landscape, data sovereignty is reshaping the rules of critical infrastructure. As the global datasphere expands, organizations face unprecedented demands for control over their data across interconnected systems. This shift emphasizes the importance of aligning authority with accountability, ensuring clarity in governance. By embracing data sovereignty as a foundational principle, enterprises can enhance resilience, navigate regulatory complexities, and empower innovation.

DataGrail report finds your vendor may be sending data to AI models you never approved

A new report from DataGrail reveals a troubling reality for companies utilizing AI-driven software: 63.6% of vendors fail to disclose third-party AI subprocessors in their data processing agreements (DPAs). This alarming gap risks exposing sensitive customer data to AI models that businesses have not vetted. As AI adoption accelerates, the integrity of traditional DPAs is increasingly questioned. With significant regulatory scrutiny and rising costs tied to data breaches, privacy teams must adapt quickly. For additional insights, explore our article on Robinhood's new AI trading capabilities.

Presentation: Realtime and Batch Processing of GPU Workloads

Join Joseph Stein as he explores the engineering of an enterprise AI-as-a-Service platform within a private cloud data center. In this presentation, he will discuss strategies to maximize underutilized GPU pools through multi-namespace scheduling and leverage Valkey and Lua for effective queuing and backpressure management. Additionally, he will address how to mitigate OWASP Top 10 LLM risks using central proxy gateways and scale batch pipelines via a custom S3-to-Kafka proxy. For further insights, check out our article on automating everyday tasks with AI.

Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk

In the evolving landscape of enterprise AI, new forms of technical debt—prompt debt, retrieval debt, and evaluation debt—are emerging as critical challenges. Unlike traditional technical debt, which is often localized and easily identifiable, these AI-specific debts manifest across distributed systems, complicating risk management and accountability. As highlighted by recent studies, a staggering 95% of AI projects fail to deliver value, largely due to poorly designed systems.

AI agents are quietly generating chaos engineering failures enterprises don’t track yet

As enterprises increasingly adopt AI agents, a concerning gap in chaos engineering practices is emerging. Many organizations are unaware that agent actions, while technically correct, can trigger cascading failures due to incomplete context. This disconnect leads to confusion over accountability between teams. With 79% of organizations deploying AI agents and predictions of widespread integration by 2028, it’s crucial to recognize these agents as chaos injectors. To navigate this landscape effectively, companies must audit their agent actions and link them to chaos engineering frameworks.

Your AI agents need a terminal, not just a vector database

In the evolving landscape of AI-driven workflows, traditional retrieval systems often fall short, limiting agents' abilities to access real-time data. Researchers propose Direct Corpus Interaction (DCI), a game-changing technique allowing agents to interact directly with raw data using command-line tools, bypassing complex embedding models. This approach enhances precision in dynamic environments, ensuring agents can access the most relevant and current information. As enterprises adapt, DCI could redefine data management, supporting tasks that demand exact evidence and detailed insights.

D&B's database of 642 million businesses was built for humans, not AI agents. So they rebuilt it.

Dun & Bradstreet has reimagined its extensive Commercial Graph, which encompasses 642 million businesses, to better serve AI agents. Originally designed for human analysts, this architecture struggled to meet the demands of automated workflows. By consolidating fragmented databases into a unified knowledge graph and developing a structured access layer for agents, D&B has created a system that facilitates rapid, precise querying.

A 0.12% parameter add-on gives AI agents the working memory RAG can't

In the evolving landscape of AI, the introduction of delta-mem offers a groundbreaking solution to the long memory challenge faced by agents. Traditional methods, like expanding context windows or relying on retrieval-augmented generation (RAG), often lead to inefficiencies and increased costs. Delta-mem compresses historical interactions into a compact matrix, enhancing memory retention without bloating model size. This innovative technique empowers AI agents to carry forward relevant information seamlessly, streamlining workflows and reducing latency.

Alibaba's proprietary Qwen3.7-Max can run for 35 hours autonomously and supports external harnesses like Anthropic's Claude Code

Alibaba's Qwen3.7-Max marks a significant advancement in the AI landscape, boasting 35 hours of continuous autonomous operation. This proprietary model can execute complex tasks, positioning itself firmly in the emerging "agent era," where AI actively plans and adapts over extended periods. By integrating with external frameworks like Anthropic's Claude Code, Qwen3.7-Max offers enterprises a powerful tool for automation and innovation. However, its API-only access raises questions about accessibility, reflecting a shift from Alibaba's historically open approach.