IBM at Gartner IT Symposium/Xpo™

Agentic applications offer cohesive, end-to-end solutions that automate entire workflows, from finance to procurement. The potential of these applications is enormous, but compared with generative AI, agentic AI poses new risks. The proliferation of AI tools, often deployed in silos or business functions, creates a need to manage and govern them all from one place. Agents must clear a higher bar for trust in terms of data quality and governance.

Research shows 94% of companies overspend on cloud. Fortunately, agentic AI is redefining enterprise IT by enabling systems to investigate, understand and recommend impactful actions. Take control of your IT investments with AI-driven FinOps and observability that transforms operations. Gain real-time issue detection and automated resolution—boosting resilience, efficiency, and developer velocity.

Complex integrations and redundant and overly-manual processes can introduce cost and chaos into your application environment. Adopting a consistent hybrid cloud operating model helps tackle the complexity by driving a standardized workflow, consistency across environments, and developer self-service, without sacrificing governance.

Why do you need an intentionally designed architecture? A hybrid architecture approach meets your data and IT where they are, while managing costs and enabling better AI capabilities. Learn how a well-designed hybrid architecture unifies data, takes advantage of high-performance computing, and improves security to support you on your journey to success with AI at scale.​

Success with agentic AI depends on the ability to orchestrate multiple AI systems and all enterprise data. The productivity gains that come with the automation of complex workflows require connecting multiple domain-specific agents — often in different environments — in a reliable and scalable way. Agents are also only as effective as the context they operate with: To be more accurate, they need access to unified, secure and timely data.