A deep dive into building expert AI systems using a Wisdom-Driven Knowledge Graph. This approach enhances Knowledge-Augmented Generation (KAG) to surpass traditional Retrieval-Augmented Generation (RAG) by enabling systems to understand, reason, and provide expert-level quantitative analysis and advice.
Ivan Nardini from Google Cloud demonstrates how to build, enhance, and productionalize AI agents using Google Cloud's agent stack. The session covers the challenges of deploying agents and introduces the Agent Development Kit (ADK) for building, the Vertex AI Agent Engine for managed deployment, and protocols like MCP and Agent-to-Agent for tool integration and inter-agent communication, using Claude on Vertex AI as the core LLM.
A deep dive into using Google's A2A (Agent-to-Agent) framework and MCP (Model Context Protocol) to build intelligent, automated workflows. This summary covers the core concepts, strategic implementation, a practical multi-agent architecture, and critical insights on lean context management to control costs and latency.
Babak Hodjat, CTO of AI at Cognizant and a co-inventor of the technology behind Siri, discusses the strategic shift from generative AI to autonomous, multi-agent systems. He explores how these agentic systems will redefine enterprise operations, the intense "arms race" for AI talent, and the critical need for a decentralized, secure framework for agent collaboration.
LG AI Research introduces its advanced financial forecasting framework, which powers a US equities market ETF (LQAI) and a new "Master Score with Commentary" product with LSEG. The system uniquely combines structured financial data with unstructured text from news and reports, using the proprietary Exaone LLM and a multi-agent architecture to deliver explainable, accurate, and actionable market predictions across the entire US stock market.