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In already a few short years, AI technology has evolved from basic chat completions to autonomous, long-running agents. This poses a challenge for IT teams who need to enable their builders to innovate while also providing guardrails and controls to reduce enterprise risk. More than just chatbots or assistants, agents are now autonomous entities capable of operating over extended horizons, crafting their own sub-agents, and using professional tools to complete multi-step plans. But as agents leave the developer's laptop and start interacting with production data and external APIs, freedom wit

The AI agent world is messy. Teams are reaching for LangChain, LlamaIndex, CrewAI, AutoGen, or building custom solutions from scratch. Good. That's how it should be during the creative phase. But once an agent leaves a developer's laptop and starts talking to production data, calling external application programming interfaces (APIs), or running on shared infrastructure, freedom without guardrails stops being a feature and starts being a liability.We've watched the industry go through waves: Model APIs (such as chat completions), agentic APIs (such as assistants and later the OpenAI responses

Navigating the complexities of AI infrastructure shouldn’t be a barrier to innovation. Red Hat has completed the first phase of AI Cloud Ready status for the NVIDIA Cloud Partner (NCP) program to help address the increasing complexities of AI. NCPs build and operate GPU accelerated AI platforms to deliver and support full-stack, AI-optimized offerings based on the NCP software reference guide. This reference architecture is a proven blueprint for the full stack, including GPU servers, networking, storage, and software, to enable NCPs to deliver AI capacity as reliable, consistent services in

Following the successful launch of the Red Hat AI Factory with NVIDIA, Red Hat is pleased to announce the latest update in our collaboration with NVIDIA – delivering Day 0 support for the NVIDIA Nemotron open model family on the Red Hat AI Factory with NVIDIA. With this effort, we are providing a fully optimized, open source pathway for enterprise-grade generative AI.From infrastructure to intelligence: Accelerating AI mainstream enterprise adoptionThe Red Hat AI Factory with NVIDIA was designed to provide a turnkey environment for developing and deploying AI at scale. Today’s announcement

Developing software as efficiently and swiftly as possible is a competitive necessity. The faster and sooner you can get new products to market, the greater advantage you have with your customers. In recent years, AI coding has become a compelling way to help solve these challenges by handling tedious, repetitive tasks and debugging and testing more quickly. This frees up valuable time for higher-impact development work.However, the rapid adoption of generative AI-powered coding has introduced new enterprise-level challenges. As organizations scale their use of AI tools, they confront critical

In the next decade, AI will redraw the map of technology ecosystems. As we traverse what Forrester is calling the "seventh wave" of major technological change—driven by generative and agentic AI—C-suite executives are facing a daunting transition. The difference between falling behind and harnessing this wave of change is your strategy for the AI computing stack.At Red Hat, our mission remains centered on open source principles: Collaboration, transparency, and choice. We believe that for AI to truly deliver on its promise of productivity and business value, it cannot remain a proprietary

Managing a hybrid cloud environment spanning on-premise data centers, edge deployments, and multiple public clouds often results in subscription sprawl. Even in simpler environments, it can be challenging to maintain clear visibility into subscription use. Organizations frequently struggle to answer a basic question: “Exactly how much of our purchased Red Hat capacity are we actually using right now?”Subscription watch is the solution to this complexity. It is a Software-as-a-Service (SaaS) tool integrated into Red Hat Hybrid Cloud Console that provides a unified, aggregated view of your

vLLM Semantic Router: Signal driven decision routing for mixture-of-modality modelsAs LLMs diversify across modalities, capabilities and cost profiles, the problem of intelligent request routing—selecting the right model for each query at inference time—has become a critical systems challenge. Red Hat is collaborating in the upstream community to deliver vLLM Semantic Router, a signal-driven decision routing framework for Mixture-of-Modality (MoM) model deployments. Learn more Techstrong.ai - Red Hat Extends AI Reach Deeper into the EnterpriseRed Hat is delivering a stable, reliable foun

Red Hat Satellite manages Red Hat Enterprise Linux (RHEL) systems at scale across the cloud and on-premises. Last year, a model context protocol (MCP) server for Red Hat Satellite was released as a Technology Preview feature to enable more intelligent and automated management of Satellite and RHEL systems through your favourite large language model (LLM).LLMs make it possible to perform highly automated and sophisticated tasks. An LLM can enable automatic, unsupervised problem solving, simulating the acts of perception, learning, and reasoning. Tools such as MCPs make it possible for LLMs to o

Federated AI inverts the traditional machine learning paradigm. Instead of bringing data to the model, it brings the model to the data. Training happens locally on distributed nodes (i.e., hospitals, banks, and edge devices), and only model updates are shared with a central coordinator. The raw data never leaves its source. We will discuss this approach and how it enables collaborative AI while addressing privacy regulations (i.e., GDPR-EU data protection and HIPAA-US healthcare privacy) and data sovereignty requirements critical for healthcare, finance, and cross-border deployments. In this p

When Red Hat revealed our AI quickstarts, EDB suggested a use case to balance the business need for data with the non-negotiable demand for governance. We often treat this as a zero-sum game, but what if the architecture itself could negotiate peace?This Data Governance Co-Pilot AI quickstart, built on Red Hat OpenShift AI and EDB Postgres AI (PGAI) platform, treats safe data discovery as a requirement. It provides a protected workspace where any data consumer can navigate complex schemas and extract insights with less risk of tripping compliance wires.Retrieval-augmented generation (RAG) with

Red Hat Summit 2026 arrives in Atlanta in two months! If you’re joining us May 11-14 at the Georgia World Congress Center, you can begin planning your week of keynotes, product roadmaps, lightning talks, power trainings, labs, breakout sessions, social events, and more using the session catalog and agenda builder. If you haven’t registered yet – don’t worry! There’s still time to submit your registration and confirm your spot at Red Hat Summit. The Red Hat Summit 2026 session catalog details hundreds of compelling sessions and labs focused on today’s leading tech topics – AI, vir

Today, the Fedora Project is excited to announce that the beta version of Fedora Linux 44 - the latest version of the free and open source operating system - is now available. Learn more about the new and updated features of Fedora 44 Beta below and don’t forget to make sure that your system is fully up-to-date before upgrading from a previous release.What’s new in Fedora 44 Beta?Installer and desktop ImprovementsGoodbye Anaconda created default network profiles: This change will impact how Anaconda populates network device profiles so that only devices configured during installation – b

Earlier this year, we launched the Red Hat AI quickstart catalog, a collection of ready-to-run blueprints designed to help organizations move from talking about AI to using large language models (LLMs) to solve real-world problems. This provides systems integrators and architects with example AI solutions that Red Hat engineering has tested and streamlined for easy deployment.Once you've successfully rolled out an interactive solution on Red Hat AI, however, the next question is usually, "How do I protect this in the real world?"To help answer this, we've expanded the AI quickstarts catalog wi

In our previous article Context as architecture: A practical look at retrieval-augmented generation, we treated retrieval-augmented generation (RAG) as an architectural idea. We explored why retrieval exists, how it changes the system around a language model, and where its boundaries lie. That framing is necessary, but incomplete.Once teams move beyond prototypes and begin operating RAG systems in production, a new reality sets in. Retrieval does not fail loudly. It fails subtly, probabilistically, and often convincingly. Systems return an answer, grounded in some source, even when that source

Red Hat MWC Barcelona NewsroomFollow Red Hat's news from the world’s largest mobile industry event including customers and partners like Bell Canada, Telenor AI Factory, Telefónica, NVIDIA and more. Learn more Telstra advanced autonomous networks ambition through breakthrough collaboration with Red Hat, Dell Technologies and CiscoTelstra has reached a key milestone in its journey toward building one of the world’s most advanced autonomous networks by successfully demonstrating an AI-enabled self-healing capability, in collaboration with Red Hat, Dell Technologies and Cisco. Learn more

This blog is an adaptation of our keynote presentation at PyTorch Day India. In the debate between open source and proprietary technology, open source wins — especially in the AI arena. However, as the generative AI era continues, enterprises face a new version of an old challenge. While the industry is moving at breakneck speed, much of the underlying infrastructure remains fragmented or locked behind proprietary gates. If AI is to be the key to unlocking unprecedented potential, it must be open at every layer—from the datasets and training pipelines to the infrastructure and the serving

At Red Hat, we’ve always believed that the most complex challenges in technology are best solved through open collaboration. This week, as announced by the Linux Foundation, Red Hat has officially joined the OCUDU (Open Centralized Unit / Distributed Unit) Ecosystem Foundation as a general member.Launched under the stewardship of the Linux Foundation, OCUDU represents an important shift in how cellular networks are built. By creating a carrier-grade, open source software stack for 5G, 5G-Advanced, and 6G, the project aims to do for the Radio Access Network (RAN) what Linux did for the data c

The Model Context Protocol (MCP) is increasingly relevant in today’s agentic AI ecosystem because it standardizes how AI agents access tools, data sources, and external systems. As agents move from passive chatbots to autonomous actors capable of planning and executing tasks, MCP provides a structured, interoperable interface layer that enables tool invocation with enhanced security, controlled access to external systems, and more consistent policy enforcement across heterogeneous environments.. In essence, MCP forms the connective tissue between LLM-driven reasoning and real-world system ex

At Red Hat Summit 2025, we introduced Ask Red Hat, a conversational AI designed to be an intelligent front door for our customers. It began as a rapid 12-week build to prove that open source AI could transform the support experience.Today, Ask Red Hat has evolved from a proof of concept into a sophisticated production reality. As of late 2025, it has served over 50,000 unique users and handled more than 450,000 messages. It is no longer just a standalone tool but a cross-product orchestration layer, integrated directly into docs.redhat.com and new support case creation in the Red Hat Customer