# knowledge layer Published articles for knowledge layer. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Progress Software Connects Enterprise Knowledge Across Business Systems with New Agentic RAG Capabilities DevFeed: [Progress Software Connects Enterprise Knowledge Across Business Systems with New Agentic RAG Capabilities]() Original publisher: [Read original article]() Author: SD Times Newswire Published: 2026-10-01T17:32:46Z Content type: release Language: en Sources: [SD Times]() Topics: [Agentic AI](), [Amazon Bedrock Knowledge Bases](), [Multi Agent Systems]() Tags: [agentic](), [agentic-ai](), [ai](), [knowledge-layer](), [latest-news](), [llms](), [microsoft](), [microsoft-teams](), [newswire](), [progress](), [rag]() ### AI overview Progress Software released three Progress Agentic RAG capabilities: a Microsoft Teams application, a Smart Agent for multistep retrieval across connected sources, and a WordPress plugin for content ingestion. The release says these additions preserve existing permissions and governance and reduce the need for custom orchestration and source-routing logic. ### Source excerpt Burlington, Mass. -- Progress Software, an AI infrastructure software leader, today announced new capabilities within Progress Agentic RAG, an agentic retrieval-augmented generation (RAG) platform for AI agents and LLMs that connects enterprise knowledge to AI-powered experiences through a shared Agentic Knowledge Layer. The release includes a native Microsoft Teams application, a new Smart Agent for... ... continue reading The post Progress Software Connects Enterprise Knowledge Across Business Systems with New Agentic RAG Capabilities appeared first on SD Times. ## The knowledge layer for enterprise: Processes DevFeed: [The knowledge layer for enterprise: Processes]() Original publisher: [Read original article]() Author: Jocelyn Hoppa Published: 2026-09-30T08:51:00Z Content type: tutorial Language: en Sources: [Neo4j]() Topics: [data-governance](), [semantic-layer](), [Amazon Bedrock Knowledge Bases]() Tags: [ai](), [developer](), [enterprise](), [enterprise-architecture](), [genai](), [knowledge](), [knowledge-layer](), [processes](), [semantic-layer]() ### AI overview This tutorial explains how to represent enterprise processes as a queryable graph for AI systems. It uses AcmeBank's loan approval process to show how activities, tasks, conditions, handoffs, and responsible roles can be modeled as data, allowing people and agents to answer process questions. ### Source excerpt The knowledge layer for enterprise AI: ProcessesThis series is the practical build companion to Jesús Barrasa's knowledge layer manifesto. This chapter is built on the previous chapter 1: Operating StructureRosa Delgado runs Risk & Compliance at AcmeBank. In the first... Read more -> ## Blitzy's autonomous coding bet: Every codebase is already a graph DevFeed: [Blitzy's autonomous coding bet: Every codebase is already a graph]() Original publisher: [Read original article]() Author: Jonathan Anthony Published: 2026-09-28T16:18:22Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Multi Agent Systems](), [Graphs](), [Neo4j]() Tags: [agent-action-plan](), [agent-memory](), [agentic-ai](), [ai](), [ai-agents](), [ai-code-generation](), [ai-reasoning](), [autonomous-coding](), [blitzy-inc](), [code-quality-assurance](), [coding-agents](), [contextual-ai](), [cube-event-coverage](), [cypher](), [data-governance](), [data-relationships](), [enterprise-ai](), [explainable-ai](), [github](), [gitlab](), [graph-databases](), [graph-technology](), [graphrag](), [graphsummit-2026](), [john-furrier](), [knowledge-graphs](), [knowledge-layer](), [neeraj-deshmukh](), [neo4j](), [neo4jgraphsummit](), [neo4jgraphsummit26eventpage](), [news](), [rag](), [retrieval-augmented-generation](), [semantic-search](), [software-development](), [swe-bench-pro](), [thecube](), [trustworthy-ai](), [vector-search](), [venture-capital-funding]() ### AI overview Blitzy is using a dynamic knowledge graph built from customer codebases and connected to source control to give coding agents context for changes across large enterprise systems. Its engineering director argues that understanding code relationships and dependencies is a central challenge for autonomous coding, especially when agents work across very large codebases. ### Source excerpt Knowledge graphs are moving to the center of autonomous software development as enterprises push coding agents beyond quick fixes and into large, interconnected codebases. The more code an agent touches, the more it needs to know about everything that code connects to. Investors are betting heavily on platforms built for that problem. Blitzy Inc. raised [...] The post Blitzy's autonomous coding bet: Every codebase is already a graph appeared first on SiliconANGLE. ## Neo4j makes the case for knowledge graphs as shared context for AI agents DevFeed: [Neo4j makes the case for knowledge graphs as shared context for AI agents]() Original publisher: [Read original article]() Author: Victoria Gayton Published: 2026-09-24T19:38:27Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Graphs](), [Neo4j](), [Artificial Intelligence](), [context](), [data]() Tags: [agent](), [ai](), [ai-agents](), [context-for-ai-agents](), [cube-event-coverage](), [data](), [enterprise-ai](), [graphsummit-2026](), [jesus-barrasa](), [john-furrier](), [knowledge-graphs](), [knowledge-layer](), [neo4j](), [neo4jgraphsummit](), [neo4jgraphsummit26eventpage](), [news](), [ontology](), [thecube]() ### AI overview This article reports Neo4j's argument that knowledge graphs can provide shared enterprise context for AI agents. A governed knowledge layer can connect data, business rules, concepts and processes, improving consistency, explainability and reuse across agent applications. ### Source excerpt Knowledge graphs can give enterprise agents a shared understanding of how data, business rules and processes fit together. However, that context becomes harder to maintain when each new agent carries its own version of what the business knows. Organizations have become more proficient at building agents, but their results still vary widely. The difference often [...] The post Neo4j makes the case for knowledge graphs as shared context for AI agents appeared first on SiliconANGLE.