NEWS · CODING · #262
Presentation: From Retrieval to Reasoning — Building Production Agentic AI Systems with Knowledge Graphs
Cassie Shum argues that knowledge graphs are a foundational component for agentic AI systems and outlines four practical architectural patterns — context bundling, decision provenance, code as truth, and agent visibility. She also demonstrates an engineering harness built on a knowledge graph aimed at streamlining feedback loops, optimizing token usage, and improving system reliability.
KEY POINTS
- Cassie Shum argues that knowledge graphs are a foundational component for agentic AI systems and outlines four practical architectural patterns — context bundling, decision provenance, code as truth, and agent visibility.
- She also demonstrates an engineering harness built on a knowledge graph aimed at streamlining feedback loops, optimizing token usage, and improving system reliability.
- This matters because it presents concrete architectural patterns and an implemented harness showing how knowledge graphs can address common production challenges in agentic AI beyond basic RAG approaches.
WHY IT MATTERS
This matters because it presents concrete architectural patterns and an implemented harness showing how knowledge graphs can address common production challenges in agentic AI beyond basic RAG approaches.