Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs
Beyond RAG: 4 architectural patterns for production-ready agents, from decision provenance to token optimization.
Why it matters
Practitioners building agentic systems need patterns for reliability, observability, and cost control. Knowledge graphs as infrastructure for agent feedback loops and decision tracing move agents from prototype to production.
The key facts
9 to know4 architectural patterns: context bundling, decision provenance, code as truth, agent visibility
Knowledge graphs as foundation for agentic systems
Engineering harness for feedback loops and token optimization
Focus on system reliability and observability in production agents
Speaker: Cassie Shum
Source: InfoQ presentation (vendor/conference content)
Knowledge graphs positioned as critical foundation for agentic systems (not just RAG enhancement)
Focus on feedback loops, token optimization, and system reliability—production engineering concerns
Engineering harness approach suggests deployment-ready tooling
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: 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…