Agentic AI: What Breaks, What Works, What Scales

Moving Agentic AI from Experimentation to Enterprise Execution.

For many organizations, the challenge is no longer proving that AI works. It is determining whether AI agents can be trusted to operate within critical business processes and deliver measurable business outcomes.

While AI agents can generate impressive responses in controlled environments, production deployment exposes a different reality: fragmented enterprise data, stable context, escalating inference costs, and governance concerns that limit autonomous action.

The different between a promising AI pilot and a successful enterprise deployment comes down to trust, context, control and business value.

Organizations that solve these challenges first will move beyond copilots and experimentation toward AI-driven execution at scale.

Join AWS and Denodo for an invite-only executive roundtable exploring how leading enterprises are closing the AI Trust Grap and enabling agentic AI to operate with trusted, real-time business context.

Through practical insights, real-world lessons, and executive-level discussion, discover what breaks, what works, and what it takes to scale agentic AI responsibly. 

What We'll Cover

1. Why AI Agents Fail In Production - Moving from a successful pilot to enterprise-wide deployment introduces challenges that controlled environments rarely reveal. Explore why AI agents struggle with real-world complexity, fragmented systems, unreliable context, and operational constraints.

2. Solving the Enterprise Context Problem - AI agents are only as effective as the information they can access and understand. Discover how connecting agents to trusted, real-time business data can improve decision-making, reduce information gaps, and enable more reliable execution.

3. Balancing Autonomy with Trust and Governance - Greater autonomy introduces new questions around security, accountability, permissions and oversight. Explore how organizations can establish the controls needed to enable AI agents to act responsibly within critical business processes. 

4. Controlling AI Costs Without Compromising Value - As agentic workflows become more complex, inference costs and operational overhead can quickly escalate. Learn how organizations can improve efficiency, manage costs, and maintain the accuracy and business value required for production deployment.

5. Enterprise Use Cases Delivering Measurable ROI - Move beyond experimentation to explore enterprise applications where agentic AI can deliver tangible business outcomes. Understand how to identify high-value opportunities, define success metrics, and connect AI initiatives to measurable ROI.

6. Lessons from Scaling Agentic AI - Discover the practical lessons emerging from enterprise deployments, including the architectural decisions, data strategies, governance models, and operational practices that help organizations scale agentic AI with confidence.

Closing the AI Trust Gap

The next phase of enterprise AI will not be defined by how impressive an agent looks in a demonstration. It will be defined by whether that agent can reliably operate within the business, make informed decisions, and deliver outcomes that matter.

Achieving this requires more than capable models. It requires trusted data, relevant context, effective governance, and an architecture that supports secure, scalable execution. 

AWS and Denodo invite you to explore how these elements come together to help organizations move from AI experimentation to responsible enterprise execution.

Join the Conversation

Agentic AI: What Breaks, What Works, What Scales

Real lessons, business impact, and practical guidance for deploying agentic AI responsibly.

Join AWS and Denodo for an exclusive roundtable examining how enterprises can close the AI Trust Gap, connect agents to trusted business context, and move toward AI-driven executive at scale. 

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