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.

Meta describes how an AI agent can be designed to capture the logic and expertise of domain experts, rather than simply storing documents or retrieving relevant information. The system, dubbed an "organizational second brain", was built for a specialized compliance domain, but Meta argues the architecture generalizes to areas like security, finance, engineering, and procurement.

InfoQ expands its online certification portfolio with new AI Engineering and Organizational Architecture cohorts, giving senior practitioners a confidential peer group to pressure-test production AI, platform, team design, and architecture decisions.

In this article, the author explores how hierarchical agentic RAG systems coordinate specialized workers through structured orchestration to improve accuracy, reliability, and explainability in complex enterprise analytics workflows. The article uses Protocol-H as a to show how deterministic routing, reflective retry, and modality-aware reasoning support safer multi-source query execution.

This article introduces Context-Augmented Generation (CAG) as an architectural refinement of RAG for enterprise systems. It shows how a Spring Boot-based context manager can incorporate user identity, session state, and policy constraints into AI workflows, improving traceability, consistency, and governance without altering existing retrievers or LLM infrastructure.