Red Hat Summit: Connect 2026 | Melbourne

Join our Red Hat OpenShift roadmap session for an overview of its evolution into an intelligent application platform. Discover how OpenShift integrates agentic AI to streamline operations, accelerate troubleshooting, and improve efficiency for container, VM, and AI/ML workloads. We will also cover new support for post-quantum cryptography, native multi-cluster capabilities, extended lifecycles, and digital sovereignty. Learn how these built-in capabilities simplify development and management across hybrid cloud environments, positioning OpenShift as your catalyst for innovation and success in today’s digital ecosystem.

Do you have automation in place but wonder how to add AI? You do not need to start over; instead, you can build on what you already have. In this session, we show you how to combine different types of automation—including AI-driven tasks—to solve your work challenges. You will learn how to mix task-driven, event-driven, and AI-driven automation; how Red Hat Ansible Automation Platform acts as the main tool to make these methods work together effectively; and why your current safety rules are even more important when using AI. We will walk through a real-world example so you can see how it works. If you want to use AI without disrupting your stable infrastructure, this session is for you.

Red Hat is re-imagining the open source development model to shape the next version of RHEL, making its roadmap more accessible through transparent, iterative collaboration with the community and customers. Additionally, we’ll demonstrate how new Model Context Protocol (MCP) capabilities in Red Hat Satellite transform manual patching workflows into proactive, AI-assisted operations. By automating vulnerability response, prioritisation, and remediation, teams can enhance operational efficiency, security, and reliability across dynamic hybrid cloud environments.

As organisations transition AI from experiments to foundational applications, they face complex mandates. True sovereign AI requires more than data localisation; it demands a comprehensive strategy across data, operations, assurance, and technology. Many leaders mistakenly believe this necessitates fully isolated, on-premise deployments, which can hinder scalability and create fragmentation. This session demonstrates how to translate these constraints into scalable architectures using a pattern-based approach. We will explore deployment models, such as the Sovereign Cloud Pattern, to help balance regulatory requirements with AI innovation. Attendees will learn to establish a layered platform architecture for a closed-loop AI lifecycle, ensuring models and data remain within legal boundaries while providing the verifiable audit trails required for trust. Join us to discover how to leverage open hybrid foundations to deploy policy-driven, auditable sovereign AI at scale.

The "Mythos moment" has arrived, and frontier AI is now capable of autonomously discovering, chaining, and exploiting vulnerabilities at machine speed. How do you defend your infrastructure, when the time to exploit vulnerabilities is now measured in hours, not on weeks? In this session we'll explore how Red Hat takes a "Defence in Depth" approach across our products. We'll explore new capabilities like Red Hat Hardened Images, the Zero Trust Workload Identity Operator, and even using AI to better secure AI. Join this session to learn how you can better use the "batteries included" security capabilities with your Red Hat subscriptions.

Join Red Hat as we tackle why most AI initiatives stall, not because the models don't work, but because organisations can't operationalise them. We'll identify the four barriers to real AI value: spiralling economics, safety and trust gaps, inconsistent quality, and the struggle to reach production. Underlying all four is one root cause: embedding non-deterministic models into business processes that demand predictability. We'll unpack the six controls that resolve this tension and turn ambition into reliable, governed AI systems, and introduce GenAIOps: the discipline, practices, and platform capabilities that make those controls real at enterprise scale. Walk away with a clear framework for assessing where your own AI initiatives stand and what's needed to get them to production with confidence.

AI adoption often leads to siloed models, fragmented infrastructure, and governance challenges. This session explores how Red Hat AI enables organisations to become their own Private Model-as-a-Service provider, delivering secure, governed AI models through self-service APIs. Learn how to centralise model serving, optimise GPU utilisation, enforce governance, and provide a scalable AI platform across hybrid cloud environments, empowering developers to consume AI without managing the underlying infrastructure.

Agentic AI is reshaping enterprise application development, but realising its value requires more than deploying models. It requires an operational AI platform spanning infrastructure, inference, model serving, evaluation, governance, and lifecycle management. This session explores how Red Hat AI operationalises the full AI stack, from Model-as-a-Service, AgentOps, AI safety and evaluation for production workloads. Attendees will learn how Red Hat AI provides a secure, scalable, and open foundation for building, deploying, evaluating, and governing AI applications across hybrid cloud environments. The presentation also examines emerging trends including open weight models, token economics, AI governance, and the evolution toward agentic AI. Through architectural guidance and real-world examples, participants will gain practical strategies for operationalising enterprise AI with continuous evaluation, observability, security, and governance while maintaining flexibility, compliance, and control.

Your observability and AI platforms are generating insights faster than teams can act on them. Yet insights alone do not resolve incidents. Every manual handoff between detection and remediation adds delay, inconsistency, and operational risk. In this session, we will explore the missing layer in many AIOps strategies: a trusted, governed execution framework that transforms insights into action while preserving the auditability, change control, and compliance enterprise operations demand. Using Red Hat Ansible Automation Platform, we will show how organisations are closing the loop between insights and action through automated remediation, self-healing workflows, and intelligent orchestration, while establishing the governance and execution foundation required for agentic AI.