AI, XR, DevOps, graph theory, knowledge graphs, and digital sovereignty. Deep dives, tutorials, and analysis from across the GraphWiz knowledge base.
As AI systems evolve from simple predictive models to autonomous multi-step agents, they inherit all the technical debt of earlier AI systems — plus a new class of debt unique to agentic architecture. This article maps the root causes, symptoms, and remediation strategies.
Security auditing of AI agents requires traceability that traditional systems lack. This article explores a unified graph representation for LLM agents that captures every step of agent execution, enabling comprehensive security audits, compliance verification, and incident investigation.
Memory is the missing ingredient in multi-agent AI systems. This article explores HMARS, a Hierarchical Multi-Agent Memory System for long-context reasoning, and examines how structured memory hierarchies unlock capabilities that flat memory systems simply cannot achieve.
GraphRAG has evolved from simple retrieval to sophisticated agentic context engineering. This article explores the ACE-GraphRAG framework that dynamically adapts retrieval context, explaining how it closes the representation-inference gap that plagues traditional GraphRAG implementations.
GraphRAG retrieval gets the attention, but extraction quality quietly governs end-to-end accuracy. A practical LLM pipeline for chunking, schema, extraction, entity resolution and evaluation — with runnable code.
We benchmark pi-l1-cache, a zero-dependency L1 in-memory cache extension for the pi coding agent. 138µs per request, honest about where the 100x hash claim breaks.