Why Enterprise AI Success Depends on Context Engineering

• Understand why context is a dynamic, situational capability rather than a static repository of enterprise data

• Learn why semantic models, ontologies and conformed business metrics are critical for reliable AI reasoning

• Explore how organizations can capture undocumented processes and institutional knowledge to reduce agent drift

• Discover how context engineering helps improve AI reliability, governance and business value

While enterprise AI investments continue to surge, most organizations struggle to derive significant business value from GenAI deployments due to inconsistent outputs, unpredictable agent behavior, and runaway token costs. These failures rarely stem from foundation model limitations, but rather from the lack of the contextual information needed to consistently apply organizational knowledge, policies and business logic.

In this webinar, we focus on bridging this operational gap by getting fully grounded in the core principles of context layers. Rather than relying on surface-level prompting, we will demystify what makes up an enterprise context layer, why it serves as the essential control plane for agentic systems, and how it connects static data to dynamic AI execution. Attendees will gain a clear, practical understanding of how a well-structured context layer supports higher quality reasoning and turns stateless models into trusted business assets.

Return to this web page to watch the webinar. Contact us at gartnerwebinars@gartner.com with questions about viewing this webinar.