As enterprises push beyond static AI models, agentic AI is emerging as a key driver of autonomous decision-making and adaptive workflows. These AI systems go beyond execution—they perceive, plan, and act dynamically within complex business environments. This panel will explore how enterprises can architect, deploy, and scale agentic AI to unlock new efficiencies and innovation.

• Architecting agentic AI: Frameworks, orchestration strategies, and multi-agent collaboration.
• Integrating LLM-powered agents with enterprise data systems and APIs.
• Designing guardrails for reliability, interpretability, and compliance.
• Optimizing feedback loops: Reinforcement learning, retrieval-augmented generation, and self-improvement mechanisms.
• Scaling agentic AI across the enterprise: Infrastructure, compute considerations, and cost-efficiency.

Scaling AI agents across an enterprise like JPMorgan requires more than just technical deployment - it demands seamless integration, robust governance, and continuous optimization. From aligning AI agents with existing workflows to managing compute costs and security, enterprises must navigate key challenges to achieve scalable automation and intelligence.

• Establishing a scalable AI agent architecture: Infrastructure, orchestration, and interoperability.
• Integrating AI agents into enterprise ecosystems, including CRM, ERP, and cloud platforms.
• Managing adoption challenges: Organizational readiness, user trust, and change management.
• Optimizing AI agent performance through monitoring, feedback loops, and iterative improvements.