Red Hat at IBM Think 2026

As enterprises deploy autonomous AI agents into IT management, they risk an "illusion of control" that can cause operational instability. To bridge the gap between AI insights and action, organizations need an automation strategy combining task, event, and AI-driven workflows. This session explores how Red Hat Ansible Automation Platform (AAP) acts as the trusted, governance-first execution layer for agentic operations. We will highlight AAP's integration with IBM Instana through the Model Context Protocol (MCP) server. Learn how Instana's agents dynamically introspect pre-approved AAP templates to recommend high-confidence remediations. Join us to discover how combining AI reasoning with vetted execution delivers secure, self-healing infrastructure without sacrificing enterprise control across IBM Instana, Turbonomic and Concert.

The era of simple AI experimentation is over. To drive real ROI, enterprises are moving from basic chatbots to autonomous agentic workflows that act on data in real-time. But as agents become "chatty," they place unprecedented demand on your infrastructure. Discover how Red Hat AI provides the underlying high-throughput, production-grade foundation required to power AI agents, like those delivered by IBM watsonx Orchestrate. We will discuss how access to a catalog of optimized AI models, MCP servers, data access tooling and powerful inference engines can simplify agent deployment. Learn why an integrated, production-grade AI stack that’s ready from Day Zero is essential for running secure, stable, and cost-effective agentic automation in the modern enterprise.​

AI has entered the boardroom, bringing pressure to deliver predictable, secure, and sustainable ROI. As AI moves from experimentation into core operations, enterprises must run models reliably against real data while meeting regulatory, sovereignty, resiliency, and security requirements. Accelerating AI ROI requires disciplined hybrid cloud execution that fully integrates infrastructure, accelerators, data, and AI software end-to-end. In this session, learn from experts how a right-sized, full-stack hybrid cloud approach preserves choice, maintains control across environments, and delivers durable AI ROI at scale.

Accelerating AI at enterprise scale demands eliminating data bottlenecks, streamlining pipelines, and enabling secure, scalable deployments. In parallel, the rise of shadow AI, unverified models, and fragmented data flows has made security, governance, and sovereignty nonnegotiable and a growing focus for executives and boards. In this session, discover how a unified platform that seamlessly connects data, software, and infrastructure can deliver production-ready AI at scale while remaining trusted and reliable. Learn how hybrid acceleration improves AI build cycles with faster iteration, consistency, and performance to overcome complexity and deliver business outcomes.​

As enterprises shift toward agentic AI and next-generation AIOps, automation becomes the control plane that connects infrastructure, security, and operations to ensure safety, consistency, and control. This session explores how a unified automation fabric can coordinate deterministic, event-driven, and agentic workflows across hybrid environments through a single, policy-driven system. Learn how standardized, code-based infrastructure workflows create repeatable environments, while identity-based security and dynamic credentials ensure AI systems operate with the right access at the right time. You’ll also see how simplifying workflow creation and reuse enables more teams to participate in automation without bottlenecks, strengthening resilience while accelerating delivery.

As AI workloads grow more complex and compute‑intensive, organizations need a seamless way to maximize performance across accelerated infrastructure. Red Hat AI synchronizes high‑performance hardware with intelligent orchestration to eliminate friction, boost throughput, and reduce latency. With a model‑as‑a‑service approach, enterprises can give developers and AI engineers access to frontier models while optimizing their accelerated computing resources. Discover how open source technologies like vLLM and llm-d help manage token demand and support multiple users without breaking the bank. In this session, see how Red Hat AI streamlines provisioning, automation, and resource allocation across hybrid cloud environments—and how IBM Cloud removes operational complexity to ensure high‑performance execution.

AI is transforming how enterprises operate and forcing leaders to rethink digital sovereignty. As AI moves into core business processes, organizations must prove control across data, models, and platforms while meeting expanding regulatory demands. Join this session to learn how organizations are redefining sovereignty for the AI era and how new approaches to architecture and governance are enabling innovation without sacrificing trust, resilience, or long-term independence.

What does digital sovereignty look like when it’s enforced in platform architecture, not contractual reassurances? Join IBM sovereignty experts for a deep dive into IBM Sovereign Core, exploring the architectural capabilities that operationalize digital sovereignty: customer-operated control planes, in-boundary AI inference and identity services, and continuous compliance automation with audit-ready evidence generation. Learn how these capabilities enable organizations to run AI workloads with jurisdictional control, hybrid portability, and the operational independence required to innovate at the speed of cloud.

Enterprises have moved beyond experimenting with AI agents—they’re now building and deploying them in production. As adoption expands across business units, models, and clouds, the challenge shifts from creation to control. Organizations must ensure governance, visibility, performance, cost management, and safety at scale. In this session, learn how clients are utilizing watsonx Orchestrate, the purpose-built, enterprise agent control plane, to operationalize AI agents wherever they’re built. These new innovations enable clients to lead in enterprise agent operations, transforming experiments into governed, observable, and measurable systems. The future of enterprise AI is not only intelligent—it’s orchestrated, optimized, and built for control at scale.