# Graphs A graph data structure consists of vertices connected by edges. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs DevFeed: [Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [data](), [Large Language Model](), [Code](), [Retrieval-Augmented Generation](), [Access Control](), [Graphs](), [GitHub]() Tags: [agents](), [classification](), [code](), [data](), [documentation](), [governance](), [production](), [provenance](), [retrieval](), [vector-database]() ### AI overview Glyph is a production system for generating column descriptions and assigning sensitivity-ontology labels in enterprise data catalogs. It uses cooperating LLM agents, source-code-grounded retrieval, parallel tagging strategies, vector-based metadata matching, and ranked-output fusion to support auditable cataloging. ### Source excerpt Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an... ## PostgreSQL 19 graph queries fail the 'would you ship this?' test DevFeed: [PostgreSQL 19 graph queries fail the 'would you ship this?' test]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-15T09:42:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [Databases]() Tags: [bugs](), [concurrent](), [databases](), [graph](), [postgresql](), [sql]() ### AI overview The article reports that PostgreSQL 19's SQL/PGQ graph queries were rejected because of unresolved bugs. It also discusses concurrent REPACK as a way to reduce overnight maintenance calls for database administrators. ### Source excerpt SQL/PGQ gets bounced over unresolved bugs as concurrent REPACK promises fewer midnight calls for DBAs ## What It Takes to Build a Production Agent Harness DevFeed: [What It Takes to Build a Production Agent Harness]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-14T19:50:37Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Agent Harness](), [LangChain](), [Artificial Intelligence](), [Persistence](), [Graphs]() Tags: [agent](), [ai](), [ai-engineer](), [engineering](), [evaluation](), [langchain](), [langgraph](), [memory](), [persistence](), [tools](), [tracing]() ### AI overview A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution. ### Source excerpt A hands-on nanodegree for production agent engineering. ## Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack DevFeed: [Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack]() Original publisher: [Read original article]() Author: Jonas Kim Published: 2026-09-14T16:55:58Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [Amazon Bedrock](), [Amazon OpenSearch Service](), [Amazon Web Services]() Tags: [amazon-bedrock](), [amazon-opensearch-service](), [aws](), [knowledge-graph](), [open-source](), [rag](), [retrieval](), [search](), [vector-search]() ### AI overview This article presents a unified knowledge-graph RAG stack that brings Microsoft GraphRAG and HKUDS LightRAG together on Amazon Bedrock, Amazon Neptune, and Amazon OpenSearch Service. The shared stack supports common ingestion, indexing, caching, and multilingual handling while allowing the retrieval methodology to be selected per query. ### Source excerpt Picture a compliance analyst staring at a few thousand contracts, amendments, and internal memos, trying to answer one question that sounds straightforward: "Which of our obligations are exposed if this one milestone slips?" The answer isn't written in any single document. It's stitched across three -- a master agreement that ties a payment to a [...] ## 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 ## The Palindrome Announces a Graph Theory for Visual Learners Video Course DevFeed: [The Palindrome Announces a Graph Theory for Visual Learners Video Course]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-09-12T08:47:28Z Content type: opinion Language: en Sources: [The Palindrome]() Topics: [Graphs](), [math]() Tags: [graph](), [graph-theory](), [subscriber](), [video]() ### AI overview The Palindrome announces a planned comprehensive graph theory video course called "Graph Theory for Visual Learners" and launches a support campaign for paid subscribers and founding members. The project will use custom animations and include an upcoming video release. ### Source excerpt I'm creating The Palindrome's most ambitious video yet. Become a paid subscriber and be part of it. ## Rebuilding AUTOMATIC1111 with Gradio Workflow DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Graphs](), [stable-diffusion](), [Inference](), [Large Language Model](), [hugging face](), [vlm]() Tags: [automatic1111](), [comfyui](), [diffusion](), [flux](), [gradio](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [inference-providers](), [llm](), [models](), [python](), [space](), [spaces](), [text-to-image](), [vlm](), [workflow](), [workflows]() ### AI overview A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Second Brains in the AI era: still worth it? DevFeed: [Second Brains in the AI era: still worth it?]() Original publisher: [Read original article]() Published: 2026-09-06T00:00:00Z Content type: opinion Language: en Sources: [Mutt0-ds Notes]() Topics: [Artificial Intelligence](), [context](), [AI Agent](), [Graphs](), [Obsidian]() Tags: [ai](), [ai-agent](), [context](), [knowledge-graph](), [markdown](), [obsidian]() ### AI overview The article argues that maintaining a personal knowledge base, or "second brain," is more valuable in the AI era because it gives AI assistants structured, connected context. It suggests that linked notes resemble graph memory and can help agents navigate complex information, while large unstructured collections may increase confusion and hallucinations. ### Source excerpt I've been working on my second brain(s) for years now. Brains, plural, because I had to create new ones, for example when I changed jobs...... ## Building Trust in AI DevOps: Validating the Harness Knowledge Graph DevFeed: [Building Trust in AI DevOps: Validating the Harness Knowledge Graph]() Original publisher: [Read original article]() Author: Vikram Sahu Published: 2026-08-31T18:37:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Testing](), [Graphs](), [Artificial Intelligence](), [Software]() Tags: [ai](), [ai-evals](), [api](), [automated](), [data](), [devops](), [evals](), [graph](), [knowledge-graph](), [lifecycle](), [operational](), [other](), [pipeline](), [schema](), [sdlc](), [security](), [services](), [software](), [software-delivery](), [validation]() ### AI overview This article explains how Harness validates answers from its SDLC Knowledge Graph. Its multi-layered approach combines AI evaluations, schema traversal, API checks, direct product verification, production data, and shift-left testing to improve reliability. ### Source excerpt Discover our multi-layered validation approach combining AI evals to ensure reliable AI-powered software delivery insights. | Blog ## Pulumi Context API: query your infrastructure as a graph DevFeed: [Pulumi Context API: query your infrastructure as a graph]() Original publisher: [Read original article]() Author: Levi Blackstone Published: 2026-08-26T00:00:00Z Content type: release Language: en Sources: [Pulumi]() Topics: [API](), [Graphs](), [Cloud APIs](), [infrastructure as code (IAC)](), [Command-line interface](), [JSON](), [pulumi-neo]() Tags: [agent](), [ai-agents](), [api](), [cli](), [features](), [graph](), [iac](), [infrastructure-as-code-iac](), [json](), [product](), [pulumi](), [pulumi-cloud](), [pulumi-neo](), [rest-api]() ### AI overview Pulumi is launching the Context API, a read-only API that connects Pulumi-managed and discovered resources, stacks, and their relationships into a graph. Available in public preview for Enterprise and Business Critical organizations, it supports infrastructure-impact, coverage, and cleanup queries through the Pulumi CLI or REST API. Pulumi Neo uses it out of the box, and other authenticated agents can access its schema and query guidance. ### Source excerpt Every platform team fields the same questions: What is running? What breaks if we change this? What can we safely delete? The answers exist, but they're scattered across state files, cloud consoles, and the memories of whoever set things up. Today we're launching the Pulumi Context API, a read-only API that connects Pulumi-managed and discovered resources, stacks, and their relationships into a graph. It's designed agent-first: Pulumi Neo, our infrastructure agent, uses it out of the box, and other agents can fetch the current graph vocabulary and query guidance on demand. It's available in public preview for organizations on the Enterprise and Business Critical editions. Answers that follow infrastructure relationships Pulumi already records the resources your programs manage, their dependencies, how stacks consume each other's outputs, and the resources Pulumi Discovery finds outside infrastructure as code (IaC). The Context API connects this data so you can ask questions that depend on the relationships: Impact: Which stacks are affected if we upgrade this provider? If this stack changes, what consumes its outputs? Coverage: How much of our infrastructure lives outside IaC, and in which accounts? Cleanup: Which stacks have no dependents and are candidates for retirement? A query is a JSON document with a handful of clauses. anchor names the starting nodes, traverse follows relationships from there, and return chooses what comes back. You can run a query through the Pulumi CLI or REST API. Here's a selector that starts from AWS provider instances older than version 7.0.0 and follows incoming provided_by relationships back to the visible resources they manage: { "anchor": { "nodeType": "resource", "match": { "type": "pulumi:providers:aws", "fields": { "provider_version": { "op": "lt", "value": "7.0.0" } } } }, "traverse": [ { "edgeTypes": ["provided_by"], "direction": "in", "alias": "managed" } ], "return": { "select": ["anchor", "managed"] } } A response for one m ## Why AI coding agents need context graphs DevFeed: [Why AI coding agents need context graphs]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-08-25T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [AI-assisted coding](), [Graphs](), [coding](), [API](), [internal developer platform](), [context window](), [long-context]() Tags: [ai-coding-agents](), [api-governance](), [apis](), [context-window](), [developer](), [developer-platform](), [engineering](), [general](), [graphs](), [knowledge-graphs]() ### AI overview The article argues that AI coding agents struggle in real codebases primarily because they lack access to a knowledge graph connecting code, APIs, services, ownership, dependencies, policies, and related organizational context. It describes service catalogs, API registries, ownership maps, dependency graphs, and internal developer platforms as different forms of the same underlying structure. It also argues that simply increasing the context window does not solve retrieval and context-quality problems. ### Source excerpt AI coding agents don't fail from small context windows. They fail without a knowledge graph of your code, APIs, and vendors. The post Why AI coding agents need context graphs appeared first on Postman Blog. ## The Single Most Undervalued Fact of Linear Algebra DevFeed: [The Single Most Undervalued Fact of Linear Algebra]() Original publisher: [Read original article]() Author: Tivadar Danka Published: 2026-08-24T09:47:15Z Content type: opinion Language: en Sources: [The Palindrome]() Topics: [Graphs]() Tags: [animation](), [arts](), [audio](), [graphs](), [linear](), [linear-algebra](), [matrices](), [video]() ### AI overview The author presents matrices and graphs as related representations and describes remastering an earlier piece into a video with improved animation and audio recording. ### Source excerpt Matrices are graphs and graphs are matrices ## When code is abundant DevFeed: [When code is abundant]() Original publisher: [Read original article]() Author: Bill Staples Published: 2026-08-24T00:00:00Z Content type: opinion Language: en Sources: [GitLab]() Topics: [AI Development](), [Vibe coding](), [Software Engineering](), [GitLab](), [sdlc](), [anthropic](), [Concurrency](), [Graphs]() Tags: [ai](), [ai-agents](), [architecture](), [code](), [concurrency](), [devsecops-platform](), [enterprise](), [news](), [policy](), [software-development](), [workflow]() ### AI overview This opinion argues that AI agents and large language models are making code production cheaper and faster, shifting the main challenge for enterprise software development toward context, verification, governance, identity, policy, approval, and audit. It discusses how the software development lifecycle and its underlying architecture must adapt to machine-scale concurrency and automated workflows. ### Source excerpt I returned from the holiday break in January convinced that something fundamental had changed. Large language models had reached the point where they could produce useful code reliably enough, and cheaply enough, to change the economics of software development. Engineers everywhere seemed to be experimenting with the same thing: not just asking an AI assistant for suggestions, but giving agents real work and seeing how far they could take it. I started thinking about what happens if that continues. What changes when producing code is no longer the primary constraint in building software? I wrote those thoughts down in a board memo in January. In May, I published part of that thesis in GitLab's Act 2: the cost and time of producing software was collapsing, machines would increasingly build software under human direction, and the architecture underneath software development would have to change with it. In June, at GitLab Transcend, we showed the first pieces of that architecture: source control rebuilt for machine-scale concurrency, GitLab Orbit as a context graph spanning the software lifecycle, and governance around agent identity, policy, approval and audit. Then, on August 21, Anthropic published The AI-Native SDLC Playbook. It opens with a simple statement: "Code is no longer the bottleneck." I agree. Anthropic's playbook is a practical description of how the development lifecycle changes when agents can move implementation dramatically faster: planning becomes machine-readable, handoffs become automated, verification moves into the loop, and human judgment concentrates at the gates. What interests me is what happens one level beyond the workflow. If code is no longer the primary constraint, what becomes scarce? What architecture does an enterprise need when people, agents and multiple models are all acting across the software lifecycle at machine speed? And where does durable value move when generating the code itself becomes increasingly abundant? Over the pas ## The prompts only the Postman AI Engineer can answer DevFeed: [The prompts only the Postman AI Engineer can answer]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Claude](), [Postman](), [Graphs](), [API](), [OpenAPI Specification](), [monitor](), [React]() Tags: [ai](), [ai-agents](), [api](), [apis](), [general](), [monitor](), [openapi](), [postman-ai-engineer](), [react]() ### AI overview This Postman Blog article presents the Postman AI Engineer as a natural-language interface over the Postman Context Graph. It describes prompts that trace identifiers across APIs, collections, runtime monitor results, repositories, and React dashboards, as well as prompts that expose specification-versus-code drift and three-way discrepancies involving live server responses. ### Source excerpt The Postman AI Engineer prompts that trace an ID across your APIs, catch three-way drift, and auto-write a PR review from the graph. The post The prompts only the Postman AI Engineer can answer appeared first on Postman Blog. ## GraphRAG: How AI Answers Questions Hidden Across Many Documents DevFeed: [GraphRAG: How AI Answers Questions Hidden Across Many Documents]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-19T15:31:18Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Graphs](), [Retrieval-Augmented Generation](), [Artificial Intelligence]() Tags: [graphs](), [knowledge-graphs](), [rag](), [retrieval-augmented-generation](), [search]() ### AI overview This tutorial explains how GraphRAG addresses questions whose answers emerge from patterns across an entire document collection, contrasting it with standard RAG, which is designed to retrieve information from specific documents. ### Source excerpt GraphRAG was designed to handle the second kind of questions, and we are going to learn more about it in this article. ## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### AI overview This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy. ### Source excerpt Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications... ## Add the dependency graph to a Kotlin K2 migration checklist DevFeed: [Add the dependency graph to a Kotlin K2 migration checklist]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Koin - Cloud-Inject.io -Kotzilla]() Topics: [Kotlin](), [migration](), [Dependency injection](), [Graphs](), [Compiler](), [toolchain](), [Android Gradle Plugin](), [Compose]() Tags: [android-gradle-plugin](), [compiler](), [compose](), [dependency-injection](), [graph](), [kotlin](), [migration](), [rollback](), [toolchain](), [verification]() ### AI overview A practical checklist for Kotlin K2 migrations in projects that generate or validate dependency wiring. It recommends mapping the compiler and build toolchain, upgrading from a known compatibility matrix, cleaning and inspecting generated output, verifying dependency graphs across flavors and targets, testing runtime slices, and keeping rollback changes coherent. ### Source excerpt A K2 migration is usually planned around source compatibility and compiler diagnostics. Projects that generate or validate dependency wiring need another checklist: compiler plugin versions, generated sources, metadata compatibility, and graph verification on every target. Map the toolchain first Record the Kotlin plugin, Compose compiler plugin, KSP or annotation tooling, dependency-injection compiler plugin, Android Gradle plugin, and target libraries. Upgrade from a known matrix instead of selecting each latest version independently. ## powa-archivist 5.3.0 is out! DevFeed: [powa-archivist 5.3.0 is out!]() Original publisher: [Read original article]() Published: 2026-08-17T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [releases](), [benchmarking](), [monitor](), [Graphs](), [real-time](), [GitHub]() Tags: [bugs](), [changelog](), [contributors](), [documentation](), [github](), [graphs](), [metrics](), [monitor](), [performance](), [postgresql](), [real-time](), [release](), [releases]() ### AI overview The PoWA team announces powa-archivist 5.3.0, a PostgreSQL Workload Analyzer extension that collects and aggregates metrics from multiple PostgreSQL instances, provides real-time charts and graphs for monitoring and tuning servers, and suggests index optimizations. The release also includes a fix for PostgreSQL 19 compatibility. ### Source excerpt Tainan, Taiwan - Sun 16 Aug 2026 powa-archivist 5.3.0 The PoWA team is pleased to announce the release of the version 5.3.0 of powa-archivist, the core extension of the PoWA project. PoWA (PostgreSQL Workload Analyzer) is a performance tool, compatible with all supported PostgreSQL versions. It allows to collect and aggregate metrics gathered from multiple PostgreSQL instances using various extensions covering all parts of PostgreSQL and provides real-time charts and graphs to help monitor and tune your servers. It also suggest optimizations, like global or per-query index suggestions, to easily improve performances. Misc Fix pg19 compatibility with pg19beta2 pg_stat_lock.wait_time datatype change (Julien Rouhaud) Links & Credits Thank to the users who reported bugs or submitted patches, they are all cited in the CHANGELOG file and the CONTRIBUTORS file. powa-archivist is an open project. Any contribution to build a better tool is welcome. You just have to send your ideas, features requests or patches using the github repository at github.com/powa-team/powa-archivist. Links Documentation: powa.readthedocs.io/ Download: github.com/powa-team/powa-archivist/releases Bug tracker: github.com/powa-team/powa-archivist/issues ## What an Ontology for AI Agents Actually Needs DevFeed: [What an Ontology for AI Agents Actually Needs]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-14T12:38:18Z Content type: opinion Language: en Sources: [Data Engineering Weekly]() Topics: [Artificial Intelligence](), [Graphs](), [Structured-data](), [data]() Tags: [ai](), [ai-agents](), [data](), [graph](), [knowledge-graph](), [schema]() ### AI overview The article argues that an ontology for AI agents should be treated as a governed semantic system rather than a single file or graph. It distinguishes ontology meaning from knowledge-graph facts and explains how semantic capabilities support retrieval, planning, action, verification, and operational governance. ### Source excerpt How to think about the semantic system that makes an agent coherent, governable, and useful. ## How Product Managers Can Use Graphs DevFeed: [How Product Managers Can Use Graphs]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-08-12T04:30:42Z Content type: tutorial Language: en Sources: [Product Growth]() Topics: [Graphs]() Tags: [graphs]() ### AI overview The article explains how product managers can use graphs. ### Source excerpt Peter Steinberg said Graphs are the new loops. And he's right. Here's how to use them as a PM. ## Agentic Engineering 2.0 Explained: The Future of AI Engineering DevFeed: [Agentic Engineering 2.0 Explained: The Future of AI Engineering]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:32:21Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Engineering](), [Artificial Intelligence](), [Incident response](), [Graphs](), [Code review]() Tags: [agentic](), [agents](), [ai-agents](), [ai-engineering](), [autonomous](), [building](), [development](), [incident](), [incident-response](), [latency](), [scope](), [workflow]() ### AI overview The article distinguishes two generations of agentic engineering. Version 1.0 uses AI agents in deterministic workflows with predefined stages, prompts, responsibilities, and hand-offs. Version 2.0 gives an agent a goal and lets it determine the path, with the foundation built during the first generation enabling that transition. A self-healing incident workflow illustrates the contrast. ### Source excerpt Agentic engineering 1.0 wires the path; 2.0 hands agents a goal. See how the shift works and what foundation to build in 2026. ## Data Engineering Weekly #282 DevFeed: [Data Engineering Weekly #282]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-10T01:21:26Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [data-platforms](), [data observability](), [Graphs](), [semantic-layer](), [Large Language Model](), [Apache Iceberg](), [gRPC](), [Concurrency](), [Caching](), [Optimization](), [Streaming]() Tags: [caching](), [chaos](), [concurrency](), [data](), [data-engineering](), [data-platforms](), [data-quality](), [knowledge-graphs](), [llms](), [newsletter](), [observability](), [pipelines](), [semantic-layer](), [streaming]() ### AI overview Data Engineering Weekly #282 is a newsletter covering data platform fundamentals, semantic layers, ontology-backed knowledge graphs, converged databases, AI modernization, and Netflix's real-time distributed graph query architecture. It highlights composable architectures, data quality and observability, evolving schemas supported by LLM-assisted extraction, Iceberg full-text search, and optimization techniques including concurrency control, streaming filters, and caching. ### Source excerpt The Weekly Data Engineering Newsletter ## How and Why Netflix Built a Real-Time Distributed Graph: Part 3 -- Querying the graph with gRPC... DevFeed: [How and Why Netflix Built a Real-Time Distributed Graph: Part 3 -- Querying the graph with gRPC...]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-08-07T16:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Graphs](), [gRPC](), [Netflix](), [real-time](), [Latency](), [API](), [Streaming](), [personalization](), [Security](), [apache-flink]() Tags: [apache](), [apache-flink](), [api](), [data](), [data-processing](), [events](), [latency](), [netflix](), [personalization](), [real-time](), [security](), [streaming]() ### AI overview Netflix's third-part blog post describes the serving layer for its Real-Time Distributed Graph and explains how gRPC enables efficient graph querying. It focuses on serving diverse workloads, including security lookups and personalization traces, with sub-100ms responses across a billion-edge graph. ### Source excerpt How and Why Netflix Built a Real-Time Distributed Graph: Part 3 -- Querying the graph with gRPC execution API Authors: Nilesh Mishra and Ajit Koti This is the third entry of a multi-part blog series describing how we built a Real-Time Distributed Graph (RDG). In Part 1, we discussed the motivation for creating the RDG and the architecture of the data processing pipeline that populates it. In Part 2, we discussed how we designed the storage layer to handle billions of nodes and edges while maintaining single-digit-millisecond latency. In Part 3, we will explore how we designed a fast, flexible serving layer to efficiently query the graph. Introduction In Part 1 of this series, we described why Netflix needed a Real-Time Distributed Graph (RDG) and how we used Apache Flink to build an ingestion and processing pipeline that turns streaming events into graph primitives. In Part 2, we explored how we designed a storage layer capable of handling billions of nodes and edges while still delivering single-digit-millisecond latency. In this post, we focus on the next challenge: querying the graph efficiently to power real-time insights for our internal partners. All of the work on ingestion and storage only matters if we can actually ask complex questions and get answers back quickly. As we optimized for lower latency, we found that the serving layer posed its own set of challenges, distinct from those of ingestion and storage. How do we turn a constantly evolving, billion-edge graph into sub-100ms responses across a wide variety of workloads? This is the problem we tackle in this post. The Real World Needs As we integrated the RDG into Netflix's ecosystem, we realized that "querying the graph" is not a one-size-fits-all operation. We needed to handle a wide range of access patterns: from high-volume security lookups to deep, exploratory personalization traces. Let's revisit our example from Part 1 and expand on it slightly. In the earlier posts, we focused on accounts, device ## The day RBAC stops scaling: role explosion and what comes after DevFeed: [The day RBAC stops scaling: role explosion and what comes after]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-06T00:18:10Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [Access Control](), [Graphs]() Tags: [access-control](), [authorization](), [graph]() ### AI overview The article explains how per-resource permissions cause role explosion in growing B2B applications. It describes the operational and cognitive costs of increasingly complex RBAC models and presents Zanzibar's relationship-based graph model as an alternative. ### Source excerpt Role-based access control breaks when roles outnumber users. Why per-resource permissions cause role explosion, and how to move past it without a rewrite. [Next page]()