Navigating Agentic AI at Scale: VentureBeat’s Stack Summit for Enterprise IT Leaders

VentureBeat’s Agentic Infrastructure Stack Summit isn’t just another tech expo. It’s a curated, in-person event designed specifically for enterprise IT decision-makers who are tackling the complexities of next-generation AI workloads. This summit focuses on three key areas: inference economics, AI factory architectures, and memory & context infrastructure. Each topic is crucial for building resilient, scalable systems that can handle the demands of agentic AI.

Inference Economics

One of the most pressing challenges in enterprise AI is managing the exponential compute costs associated with running complex models at scale. This section of the summit will explore how to optimize these costs while navigating the tradeoffs between different hardware options. For instance, GPUs and TPUs each offer unique benefits, but choosing the right one for your workload can mean the difference between a cost-effective solution and a budget-busting nightmare.

• Key Considerations:
  • Cost Efficiency: Techniques for reducing compute costs without sacrificing performance.
  • Hardware Selection: Evaluating the pros and cons of different hardware platforms (GPUs, TPUs, CPUs).
  • Scalability: Strategies for scaling inference workloads as your AI systems grow.

Memory & Context Infrastructure

Designing high-throughput, dynamic data architectures is essential for managing complex multi-agent state management. This involves creating systems that can handle the vast amounts of data generated by agentic AI applications while ensuring real-time performance and reliability.

• Key Considerations:
  • Data Throughput: Ensuring your infrastructure can handle the high volume of data generated by multiple agents.
  • Dynamic State Management: Techniques for managing and updating state information in real-time.
  • Scalability: Building architectures that can scale with increasing complexity and agent interactions.

Key Takeaways

The Agentic Infrastructure Stack Summit offers a unique opportunity to learn from industry leaders who are actively building and scaling agentic AI systems. Here are some key takeaways for practitioners:

• Inference Economics: Understanding the tradeoffs between different hardware options and cost optimization strategies is crucial for managing large-scale AI workloads.
• AI Factory Architectures: Building resilient, scalable foundational stacks is essential for supporting current and future autonomous applications.
• Memory & Context Infrastructure: Designing high-throughput, dynamic data architectures ensures that your systems can handle the complex state management required by agentic AI.

In Practice

To get a deeper understanding of how these concepts are applied in real-world scenarios, let’s look at some insights from key speakers:

• Vice President of Autonomous Vehicles, General Motors: Overseeing strategy, research, and deployment of next-generation automated driving systems. His experience at GM, Diligent Robotics, and Cruise highlights the importance of building safe, scalable autonomous systems.

  • Key Points:
    • Safety First: Ensuring that AI systems are rigorously tested and validated for real-world applications.
    • Scalability: Developing architectures that can grow with increasing complexity and demand.

• Chief AI Officer, eBay: Leading eBay’s AI vision and strategy, he has scaled AI across the company, driving innovation with customer experiences like Magical Listing tools and hyper-personalized recommendations.

  • Key Points:
    • Customer-Centric Innovation: Using AI to enhance user experiences and drive business growth.
    • Cross-Industry Expertise: Applying insights from healthcare, financial services, and ecommerce to build robust AI systems.

• Director of Systems Intelligence and Machine Learning, Waymo: Her team builds the foundational ML ecosystem for the Waymo Driver, ensuring advanced ML systems are ready for real-world applications.

  • Key Points:
    • Real-World Readiness: Ensuring that ML models can handle the complexities of physical world applications.
    • Advanced Techniques: Leveraging cutting-edge research to advance deep learning and neural networks.

What to Watch

As agentic AI continues to evolve, several trends are worth keeping an eye on:

• Hybrid Cloud Solutions: The rise of hybrid cloud platforms that offer flexibility and scalability for AI workloads.
• Governance and Compliance: Ensuring that AI systems comply with regulatory requirements and ethical standards.
• Emerging Technologies: New hardware and software innovations that can further optimize inference economics and data management.

The Agentic Infrastructure Stack Summit is a must-attend event for anyone looking to stay ahead of these trends and build the next generation of agentic AI systems.