# Agentic AI Architecture Published articles for Agentic AI Architecture. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs DevFeed: [Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs]() Original publisher: [Read original article]() Author: Cassie Shum Published: 2026-09-12T11:00:00Z Content type: tutorial Language: en Sources: [InfoQ]() Topics: [Graphs](), [Architecture & Design](), [Retrieval Augmented Generation (RAG)](), [Code]() Tags: [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [development](), [engineering](), [infoq](), [knowledge-graph](), [knowledge-graphs-agentic-systems-patterns](), [large-language-models](), [ml-data-engineering](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [rag](), [reasoning](), [retrieval-augmented-generation](), [transcripts]() ### AI overview A presentation on using knowledge graphs as a foundation for production-ready agentic AI systems. It covers architectural patterns for context bundling, decision provenance, code as truth, and agent visibility, along with a graph-based engineering harness for feedback loops, token optimization, and reliability. ### Source 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 knowledge graph to streamline feedback loops, optimize token usage, and maintain system reliability. By Cassie Shum ## How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation DevFeed: [How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation]() Original publisher: [Read original article]() Author: Claudio Masolo Published: 2026-09-11T10:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [recommendation systems](), [Large Language Model](), [Language models](), [sglang](), [Latency]() Tags: [agentic-ai-architecture](), [ai](), [ai-ml-data-engineering](), [artificial-intelligence](), [large-language-models](), [latency](), [liger](), [linkedin](), [linkedin-ai-multi-teacher](), [llms](), [ml-data-engineering](), [news](), [search](), [sglang](), [training]() ### AI overview LinkedIn describes a multi-teacher distillation pipeline for AI-powered job search that trains a 0.6B-parameter ranking model. The article focuses on SGLang-based teacher serving, online and offline distillation, and training optimizations reported to produce roughly an eightfold speedup. ### Source excerpt LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. By Claudio Masolo ## A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN DevFeed: [A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN]() Original publisher: [Read original article]() Author: arnab chakraborty Published: 2026-09-03T23:27:50Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Agentic AI Architecture](), [AI Architecture](), [data-architecture](), [AI Agent](), [cloud-infrastructure](), [Latency](), [real-time](), [Security](), [data-processing]() Tags: [agentic-ai](), [agentic-ai-architecture](), [ai](), [ai-architecture](), [blog](), [cloud](), [data-architecture](), [efficiency](), [infrastructure](), [latency](), [modern-apps](), [real-time](), [security]() ### AI overview The article presents a unified, on-premises data architecture for sovereign agentic AI using VMware Tanzu, VMware Tanzu Greenplum, VMware Cloud Foundation, and VMware vSAN. It argues that placing AI compute close to enterprise data can improve performance and cost while reducing latency, data-transfer fees, and compliance risks. ### Source excerpt By combining VMware Tanzu Greenplum with VMware vSAN, organizations can bring their AI compute directly to their data storage layer for improved cost and latency. The post A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN appeared first on Tanzu. The post A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN appeared first on VMware Blogs. ## Agentic AI Architecture: How CockroachDB Supports Memory, Context, and Control DevFeed: [Agentic AI Architecture: How CockroachDB Supports Memory, Context, and Control]() Original publisher: [Read original article]() Author: Alejandro Infanzon Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Agentic AI Architecture](), [CockroachDB](), [Artificial Intelligence](), [AI Architecture](), [context](), [Databases](), [data](), [Resilience](), [audit](), [SQL](), [Back end](), [Cockroach Labs]() Tags: [agentic](), [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [architecture](), [audit](), [backend](), [cockroachdb](), [context](), [data](), [database](), [databases](), [durability](), [infrastructure](), [latency](), [low-latency](), [memory]() ### AI overview This article explains how CockroachDB can support enterprise agentic AI architectures by storing durable agent state, long-term memory, retrieval metadata, schema context, permissions, execution metadata, cost information, latency telemetry, and SQL audit trails. It presents the database as an operational layer for observing, governing, and improving autonomous agent behavior. ### Source excerpt What happens when you connect a fleet of autonomous AI agents to your enterprise data stack? You quickly discover... ## What is agentic AI? An introduction to autonomous agents DevFeed: [What is agentic AI? An introduction to autonomous agents]() Original publisher: [Read original article]() Author: Manish Hatwalne Published: 2025-05-29T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Development](), [Architecture & Design]() Tags: [agentic-ai](), [agentic-ai-applications](), [agentic-ai-architecture](), [agentic-ai-challenges](), [agentic-ai-system-components](), [agentic-ai-vs-traditional-ai](), [agents](), [ai](), [autonomous](), [autonomous-agents](), [beginner](), [components](), [deploying-agentic-ai](), [fundamentals](), [how-agentic-ai-works](), [introduction](), [introduction-to-agentic-ai](), [real-world-uses-of-agentic-ai](), [what-is-agentic-ai]() ### AI overview A beginner-friendly introduction to agentic AI and autonomous agents. It explains how these systems perceive environments, reason, make decisions, and act independently through iterative loops, contrasting them with traditional AI that follows fixed instructions. The article also introduces architectural components, deployment challenges, safety, ethics, and real-world applications. ### Source excerpt A beginner-friendly introduction to agentic AI, its core components, challenges, and real-world uses. Read on.