Protect enterprise RAG systems with provenance, context isolation, and vector database governance to reduce retrieval poisoning and prompt injection risks.
This article covers the four-layer framework I use for enterprise autonomous agents, including multi-agent context passing, async patterns, and authentication.
Effective AI-driven CRM implementations depend on integration with field service, inventory, ERP, and logistics so predictive insights can be executed successfully.
GraphQL was good at a time, then it simmered off. Is GraphQL about to make a comeback because of AI? Will GraphQL be able to serve better for AI Agents?
In this article, I will be introducing a pipeline designed to identify sensitive data columns before masking steps and improve the efficiency of the data masking process.
We model a supply chain in Neo4j using Apache Spark to load data, NetworkX to identify critical nodes, and Cypher to find alternative routes after a disruption.
We eliminated per-record Python-side Protobuf parsing and JVM-to-Python crossings by letting Flink's native Protobuf format decode records directly into typed columns.
By combining Quarkus Flow, LangChain4j, MCP tools, and AGENTS.md, developers can construct deterministic, tool-augmented, and enterprise-governed AI agent loops.