A deep dive into how Prosus is deploying over 30,000 AI agents to create an 'AI Agentic Workforce'. The talk covers the transition from simple assistants to trusted senior colleagues, the internal tooling developed, and the crucial organizational strategies used to overcome adoption barriers and foster a bottom-up culture of innovation.
A discussion on the evolution of AI agent interaction, moving beyond simple text-based chat to create intuitive, GUI-driven experiences. The talk covers the practical challenges and solutions in building an impactful agent for busy professionals, focusing on quick actions, efficient data streaming, and enhanced interactivity.
Simba Khadder of Redis introduces Context Engineering 2.0, a new paradigm for AI agents that unifies structured data, unstructured data (RAG), and memory into a single, schema-driven surface. He critiques current methods like Text-to-SQL and direct API wrapping, proposing a unified context engine to provide reliable, observable, and performant data access for agents.
Jiquan Ngiam, CEO of MintMCP, discusses the paradigm shift from static programs to dynamic AI agents, outlining the significant security risks involved—supply chain vulnerabilities, third-party data poisoning, and inadvertent agent behaviors—and presents a three-pronged strategy for enterprise readiness: comprehensive monitoring, preventative guardrails, and secure, role-based deployment of Model Context Protocols (MCPs).
This panel delves into the strategic implementation of open-source, community-driven agentic stacks, balancing the benefits of open-source frameworks and models for rapid iteration, cost-efficiency, and compliance against the complexities of abstraction and debugging in production environments. Experts from NVIDIA, Stacklok, and Prosus discuss when to adopt open source, challenges in framework choice, the critical need for robust observability, and the role of no-code solutions for diverse user groups, emphasizing the importance of aligning tooling decisions with specific use cases and organizational needs.
This talk introduces Discrete CKKS, a framework that extends the approximate FHE scheme CKKS to support exact integer arithmetic. It achieves this through a hybrid bootstrapping technique that both cleans noise and raises the modulus, enabling a high-throughput, vectorized engine for general-purpose discrete computations on encrypted data.