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Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs

Cassie Shum discusses why knowledge graphs serve as a critical foundation for agentic systems. Moving beyond basic RAG, she explains 4 practical architectural patterns: context bundling, decision provenance, code as truth, and agent visibility. She demonstrates an engineering harness built on a knowledge graph to streamline feedback loops, optimize token usage, and maintain system reliability.

By Cassie Shum
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NVIDIA Personal AI Router Distributes AI Tasks across Local Compute

NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU.

By Sergio De Simone
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Session Traces and Cost Controls Help Diagnose AI Agent Failures

Session traces and cost controls are emerging as key observability techniques for diagnosing AI agent failures, helping teams spot tool-call loops and runaway spend while preserving enough execution context for post-incident debugging.

By Mark Silvester
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