# platform-engineering Published articles for platform-engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an Internal Developer Platform with Artificial Intelligence DevFeed: [Building an Internal Developer Platform with Artificial Intelligence]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-17T11:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [internal developer platform](), [AI Development](), [AI search](), [agent observability](), [OpenTelemetry](), [telemetry]() Tags: [agent](), [agents](), [artificial-intelligence](), [culture-methods](), [developer-experience](), [developer-platform](), [distributed-tracing](), [guardrails](), [internal-developer-platform](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [platform-artificial-intelligence](), [platform-engineering](), [prometheus](), [traces]() ### AI overview This news article covers a KubeCon presentation about using AI agents as an internal developer platform. It discusses semantic search across sources such as Git, Slack, Jira, repositories, pull requests, and wiki pages; guardrails for controlling actions; and logs, metrics, and traces for understanding agent behavior. The speakers also describe OpenTelemetry conventions for GenAI and related observability tools. ### Source excerpt Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior. By Ben Linders ## Beyond the sync: Argo CD needs an Enterprise Control Plane DevFeed: [Beyond the sync: Argo CD needs an Enterprise Control Plane]() Original publisher: [Read original article]() Author: Eric Minick Sudarshan Purohit Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [argo-cd](), [GitOps](), [Orchestration](), [DevOps](), [Platform Engineering](), [Deployment](), [Artificial Intelligence]() Tags: [argo-cd](), [deployment](), [gitops](), [governance](), [orchestration](), [platform-engineering](), [verification]() ### AI overview This article argues that Argo CD's manifest synchronization is only one part of enterprise software delivery. It describes how scaling GitOps can create operational toil and Argo sprawl, and proposes an enterprise control plane with workflow orchestration, governance, and deployment verification. ### Source excerpt Scaling GitOps? Argo CD is great for syncing manifests, but enterprise delivery requires workflow orchestration, governance, and AI verification. | Blog ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## Cloud Sovereignty, Provider Risk, and Migration Options for EU Companies DevFeed: [Cloud Sovereignty, Provider Risk, and Migration Options for EU Companies]() Original publisher: [Read original article]() Author: Adam Gordon Bell Published: 2026-09-16T13:00:00Z Content type: opinion Language: en Sources: [Pulumi]() Topics: [Cloud](), [migration](), [pulumi](), [Security](), [scaleway](), [Tooling]() Tags: [aws](), [azure](), [cloud](), [customers](), [kubernetes](), [migration](), [platform-engineering](), [pulumi](), [scaleway](), [security]() ### AI overview This recorded discussion examines cloud sovereignty, legal and cost concerns for EU companies using major US cloud providers, and the practical tradeoffs of moving to European providers. Three guests discuss provider exposure, encryption and account shutdown risks, migration costs, and whether Pulumi and agentic infrastructure can make future moves easier. ### Source excerpt Recorded September 3, 2026. Quotes are lightly edited for clarity. Maybe this sounds familiar. You run infrastructure at a company that isn't American. Your workloads are on AWS, Azure, or Google Cloud, probably more than one, because that is what everyone picked. Until recently nobody asked you where the data lives or who can reach it. Now you're getting questions. Legal wants to know what NIS2 means for where your systems run. Someone on the leadership team read that the US government locked the cloud accounts of judges at the International Criminal Court and wants to know if that could happen to you. Finance wants to know why the bill went up again. A customer's security review asked, in writing, which country your data sits in. So now you have questions of your own: If a US court or agency wants my data, can they get it from my provider without involving me? Does putting everything in an EU region change that? The big providers now sell "sovereign cloud" in Europe. Is that different, or a rename? If I encrypt everything and hold the keys myself, am I covered? Could my account be switched off one day? What would I do? Are Hetzner, OVH, and Scaleway usable for real workloads? How much cheaper are they once you count the migration? What should I be building on now so I can leave later if I need to? A migration like this used to be a multi-year project. Does Pulumi and agentic infrastructure change that? Is any of this worth the disruption, or should I leave what works alone? I put those questions to three people who have each dealt with this for real. One of them helps EU companies work out their exposure and builds the tooling to leave. Another has spent fifteen years sizing what cloud actually costs, and thinks most people should stay put. The third moved his company off AWS and onto a European provider. They don't agree on how big the risk is. The full hour is below. Don't just default to the hyperscalers Waldemar Kindler co-founded Think Ahead Technologies, whe ## How to attach an owner to every cloud resource you find DevFeed: [How to attach an owner to every cloud resource you find]() Original publisher: [Read original article]() Author: Zeen Rachidi Published: 2026-09-15T14:00:00Z Content type: tutorial Language: en Sources: [The New Stack]() Topics: [Cloud](), [Provisioning](), [Open Policy Agent](), [audit trail]() Tags: [audit-trail](), [cloud](), [cloud-services](), [devops](), [env-zero](), [how-to](), [logs](), [open-policy-agent](), [platform-engineering](), [policy](), [post-contributed](), [provisioning](), [sponsor-env-zero](), [sponsored-post-contributed]() ### AI overview This tutorial explains how to identify cloud resources without assigned owners and prevent new ownerless resources. It presents continuously synced inventory queries, Open Policy Agent policies requiring owner tags, and logs or audit trails for resource governance. ### Source excerpt The engineer who knew why that cloud instance existed has left the company. The instance is still running, the bill The post How to attach an owner to every cloud resource you find appeared first on The New Stack. ## Red Hat Developer Hub: Preventing compliance violations with AI coding agents DevFeed: [Red Hat Developer Hub: Preventing compliance violations with AI coding agents]() Original publisher: [Read original article]() Author: Evan Shortiss, Ben Wilcock Published: 2026-09-15T13:17:13Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [internal developer portal](), [AI-assisted coding](), [Backstage](), [Claude Code](), [Command-line interface](), [Model Context Protocol](), [Security]() Tags: [ai-coding-agents](), [ai-inference](), [backstage](), [catalog](), [claude-code](), [cli](), [developer](), [developer-productivity](), [developer-tools](), [mcp](), [platform-engineering](), [security]() ### AI overview This tutorial demonstrates how an AI coding agent connected to Red Hat Developer Hub can query a Backstage software catalog, TechDocs, and software templates before making architectural decisions. Using a simulated insurance-company catalog, the demo shows how this context can help avoid PCI-DSS compliance violations, resolve governance conflicts, and create a traceable decision record. ### Source excerpt AI coding agents can generate entire services, reason about architectures, and scaffold applications in minutes. But ask one to build a service for your organization and you'll quickly notice the gap: it doesn't know your rules. It doesn't know which messaging broker your compliance team mandates, which services already exist in adjacent domains, or which project template is the golden path for your team. The post Red Hat Developer Hub: Preventing compliance violations with AI coding agents appeared first on Red Hat Developer. ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## The AI-native SDLC won't be one process DevFeed: [The AI-native SDLC won't be one process]() Original publisher: [Read original article]() Author: Anirudh Ramanathan Published: 2026-09-12T14:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-engineering](), [anthropic](), [compliance](), [devops](), [dynatrace](), [github](), [kiro](), [platform-engineering](), [post-contributed](), [sdlc](), [signadot](), [spec-driven-development](), [sponsor-dynatrace](), [sponsor-signadot](), [sponsored](), [sponsored-post-contributed]() ### AI overview The article argues that AI-native software development needs risk- and accountability-based process variants rather than one fixed, spec-driven workflow. It emphasizes deterministic policy enforcement, agent self-checks, human approvals, and auditable records. ### Source excerpt Anthropic recently published its AI-Native SDLC Playbook. Its central claim is that "code is no longer the bottleneck." When agents The post The AI-native SDLC won't be one process appeared first on The New Stack. ## Every service needs an owner DevFeed: [Every service needs an owner]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [systems](), [incident]() Tags: [catalog](), [customer](), [incident](), [on-call](), [ownership](), [platform-engineering](), [production](), [reliability](), [service](), [service-catalog](), [sre](), [startups](), [team]() ### AI overview The article argues that growing organizations need a focused service catalog to make production ownership visible. It recommends recording each service's customer outcome, owning team, current on-call contact, deployment path, health dashboard, runbook, and dependencies, and maintaining those records as part of engineering work. ### Source excerpt A useful service catalog is not an inventory project. It is a public record of who owns a customer outcome when the system is healthy and when it fails. ## How we shipped 15 Tbps for OpenAI in 90 days (Session 2 of 3) DevFeed: [How we shipped 15 Tbps for OpenAI in 90 days (Session 2 of 3)]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-12T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [OpenAI](), [Network](), [GPU](), [Server](), [Cache](), [API](), [Oracle Database]() Tags: [api](), [architecture](), [build](), [cache](), [capacity](), [database](), [debugging](), [execution](), [gateway](), [gpu](), [network](), [object](), [openai](), [performance](), [platform-engineering](), [server](), [servers](), [sre](), [storage](), [testing](), [warp]() ### AI overview The second session describes turning an architecture for OpenAI's 15 Tbps system into a delivery plan. It covers coordinated capacity planning across network, gateway, server, storage, and database teams; caching object names through the Inventory API; delivery tracking; and performance validation. Early WARP testing found packet drops caused by an unsuitable MTU of 1500, which was changed to 9100. ### Source excerpt Architecture was only the first week. Session 2 is about the build: capacity, execution discipline, and the first signs that performance would be the real test. ## Why a DevOps Portal Cannot Replace an Operating Model DevFeed: [Why a DevOps Portal Cannot Replace an Operating Model]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-09-11T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [DevOps](), [incident](), [Deployment]() Tags: [deploy](), [deployment](), [devops](), [incident](), [operations](), [platform-engineering](), [production](), [sre](), [startups]() ### AI overview A DevOps portal can reduce operational friction, but it cannot create service ownership, incident discipline, or an operating model. The article recommends defining ownership, health signals, runbooks, escalation paths, and rollback practices before building a small, practical portal. ### Source excerpt A portal can remove friction. It cannot create service ownership, incident discipline, or a working operating model that does not yet exist. ## Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots DevFeed: [Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-10T17:01:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [Multi-tenancy](), [AI Platform](), [GPU](), [benchmarking]() Tags: [ai](), [ai-operations](), [ai-platform](), [gpu](), [multi-tenancy](), [observability](), [platform-engineering]() ### AI overview Red Hat AI 3.5 adds multi-tenancy, isolation, priority-aware GPU scheduling, safety benchmarking, observability, and GPU resource management for enterprise AI workloads. ### Source excerpt Red Hat released Red Hat AI 3.5 this week, a move designed to let software engineering teams run AI with The post Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots appeared first on The New Stack. ## Infrastructure identity for platform engineers DevFeed: [Infrastructure identity for platform engineers]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-09-08T12:20:22Z Content type: tutorial Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [Zero Trust](), [IAM](), [AI Agent]() Tags: [iam](), [identity](), [internal-developer-platform](), [platform-engineering](), [zero-trust]() ### AI overview A guide for platform engineers on adopting infrastructure identity: assigning cryptographic identities and short-lived, just-in-time access to people, machines, workloads, and AI agents. It argues that this approach can replace static secrets and network-based trust in an internal developer platform. ### Source excerpt Discover how platform engineers can eliminate static secrets and embed Zero Trust into their IDP using short-lived, cryptographic infrastructure identities. ## Red Hat Developer Hub software template authoring with rhdh-templates DevFeed: [Red Hat Developer Hub software template authoring with rhdh-templates]() Original publisher: [Read original article]() Author: Kashish Mittal Published: 2026-09-07T07:01:32Z Content type: tutorial Language: en Sources: [Red Hat Developer]() Topics: [Backstage](), [internal developer portal](), [Nunjucks](), [AI-assisted coding](), [YAML]() Tags: [ai-coding-agents](), [artificial-intelligence](), [backstage](), [developer-portal](), [developer-productivity](), [developer-tools](), [platform-engineering](), [quick-start](), [red-hat](), [software-templates](), [terminal](), [validation]() ### AI overview This tutorial introduces the rhdh-templates skill for building Red Hat Developer Hub and Backstage software templates with an AI coding assistant. It covers Nunjucks conventions, YAML schema checking, reference templates, local offline validation, repository templatization, and optional testing against a Red Hat Developer Hub instance. ### Source excerpt If you are a platform engineer or developer building Red Hat Developer Hub software templates, you know the friction of wrestling with Nunjucks syntax, guessing location.yaml placements, and discovering errors only after rendering in your developer portal. While AI coding agents handle generic YAML, they lack Red Hat Developer Hub and Backstage-specific conventions. The post Red Hat Developer Hub software template authoring with rhdh-templates appeared first on Red Hat Developer. ## We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform DevFeed: [We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-09-07T06:33:31Z Content type: article Language: en Sources: [Housing.com]() Topics: [BigQuery](), [databricks](), [Optimization](), [AWS Database Migration Service](), [data-platforms](), [Platform Engineering](), [migration](), [Cloud]() Tags: [bigquery](), [cloud-computing](), [cost](), [cost-optimization](), [data-engineering](), [data-platforms](), [databricks](), [delta-lake](), [finops](), [infrastructure](), [migration](), [optimization](), [platform-engineering]() ### AI overview This article explains how a data platform team reduced cloud costs by removing obsolete BigQuery data, adjusting Delta Lake retention, right-sizing DMS infrastructure, identifying unmonitored Databricks jobs, and standardizing pipeline onboarding and cost alerts. It reports that DMS costs were cut by over 50% and that retention was reduced from 90 days to 7 days for appropriate workloads after operational validation. ### Source excerpt How orphaned BigQuery storage, Delta retention, DMS right-sizing, and Databricks System Tables became our biggest cloud cost wins. The biggest cloud cost optimization we made wasn't shrinking clusters.It was deleting data we'd forgotten we were paying for.Like most teams, our first instinct was to tune infrastructure first. Instead, we discovered a treasure trove of hidden costs: orphaned BigQuery datasets, 90-day Delta retention, 24-hour jobs no one monitored, and DMS infrastructure that no longer matched business needs.We stopped treating cloud bills as a finance problem and started treating them as a platform engineering problem.30-second takeaway Why deleting forgotten data saved more than shrinking clusters. How we cut DMS costs by over 50%. How Databricks System Tables exposed hidden 24-hour jobs. How config.metadata standardized pipeline onboarding. How weekly Slack alerts turned cost optimization into a habit. Section 1: Storage Was Our Biggest Leak -- We Were Paying to Store Data Nobody Used This is the most overlooked cost on many data platforms. Storage duplication across platforms We had already migrated several workloads from BigQuery to Databricks. Large datasets were still sitting in BigQuery long after they had stopped serving production workloads - quietly generating storage costs month after month. Nothing failed. No alerts fired. Every month, we paid for storage that no longer served production workloads.A migration isn't complete until the old storage is decommissioned.The hidden cost of long retention The next surprise came from Delta Lake retention settings. Our workspace was configured to retain deleted table data and transaction history for 90 days to support time travel. Time travel is incredibly useful. But did every table need three months of historical recovery? Not really. We reduced retention to 7 days for appropriate workloads after validating operational needs. What changed immediately: Less storage tied up in deleted data. Faster clea ## How platform engineering 2.0 mitigates AI security and compliance risks DevFeed: [How platform engineering 2.0 mitigates AI security and compliance risks]() Original publisher: [Read original article]() Author: Steven Vaughan-Nichols Published: 2026-09-04T16:46:09Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Security](), [Securing AI](), [Responsibility & Safety]() Tags: [ai-agents](), [compliance](), [infrastructure](), [large-language-models-llms](), [platform-engineering](), [policy](), [secure-by-default](), [security]() ### AI overview The article explains how Platform Engineering 2.0 evolves existing Kubernetes, pipeline, internal developer platform, and process foundations to support production use of LLMs and AI agents. It emphasizes platform-level isolation, governance, policy-as-code, guardrails, and continuous compliance to mitigate AI security, regulatory, and operational risks. ### Source excerpt Discover how the shift from Platform Engineering 1.0 to 2.0 addresses critical AI security and compliance challenges. Learn how native model governance and workload isolation establish a scalable, secure foundation for integrating AI agents and LLMs into production workflows. ## What 143 platform teams told us about keeping infrastructure alive DevFeed: [What 143 platform teams told us about keeping infrastructure alive]() Original publisher: [Read original article]() Author: Oliver Thylmann Published: 2026-09-04T10:15:45Z Content type: opinion Language: en Sources: [Giant Swarm Blog]() Topics: [Platform Engineering](), [Kubernetes](), [cloud-infrastructure](), [Security](), [observability]() Tags: [aws](), [azure](), [bare-metal](), [infrastructure](), [kubernetes](), [observability](), [open-source](), [platform](), [platform-engineering](), [security](), [survey](), [team](), [tech]() ### AI overview Giant Swarm surveyed 143 platform and infrastructure professionals about the ongoing work required to keep production platforms running. The article argues that this platform integration tax is primarily a time cost, with security, reliability, cluster management, and observability consuming engineering capacity. ### Source excerpt The hidden cost of platform engineering isn't building your stack. It's keeping it alive. Giant Swarm surveyed 143 platform teams to find out. ## What is platform engineering? DevFeed: [What is platform engineering?]() Original publisher: [Read original article]() Author: Luca Galante Published: 2026-09-03T16:27:44Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [AI Bots](), [Artificial Intelligence](), [cloud-infrastructure]() Tags: [agents](), [ai](), [ai-agents](), [developer-platform](), [engineering](), [idp](), [infrastructure](), [internal-developer-platform](), [platform](), [platform-engineering](), [software-development](), [workflows]() ### AI overview Platform engineering is presented as the discipline of building internal platforms that provide self-service paths for software teams and increasingly AI agents. The article explains how Internal Developer Platforms standardize and automate engineering work, and how Agentic Development Platforms add infrastructure and path specifications for collaboration between engineers and AI agents. ### Source excerpt Platform engineering is the method and discipline of designing and building platforms that enable self-service capabilities for teams, and increasingly AI agents, to automate the recurring aspects of knowledge work. ## From Bottleneck to Breakthrough: Centralizing GitOps at Enterprise Scale with VCF 9.1.1 DevFeed: [From Bottleneck to Breakthrough: Centralizing GitOps at Enterprise Scale with VCF 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T14:03:46Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [GitOps](), [Platform Engineering](), [CI/CD](), [Automation](), [Cloud](), [Provisioning](), [Tooling]() Tags: [argo-cd](), [automation](), [cloud-infrastructure](), [cloud-native](), [continuous-delivery](), [deployment](), [gitops](), [home-page](), [platform](), [platform-engineering](), [provisioning](), [self-service](), [vcf-9-1](), [vcf-automation](), [vmware](), [vmware-cloud-foundation]() ### AI overview This VMware article announces VCF 9.1.1, which introduces a native GitOps service for VCF Automation Org users in Tech Preview. By integrating Argo CD, the update aims to centralize declarative application delivery, continuous delivery pipelines, and lifecycle management at enterprise scale, reducing manual work and operational friction for platform engineering teams. ### Source excerpt In today's fast-paced digital economy, where AI-driven innovation demands unprecedented agility, IT leaders are tasked with more than just provisioning infrastructure; they are expected to deliver a frictionless, self-service platform that accelerates software delivery. For platform engineers, cloud architects, and IT executives, the goal is to bridge the gap between robust infrastructure and modern application ... Continued The post From Bottleneck to Breakthrough: Centralizing GitOps at Enterprise Scale with VCF 9.1.1 appeared first on VMware Blogs. ## The economics of agent scale: tokens, ROI, and building platforms for AI-first teams (Part 2) DevFeed: [The economics of agent scale: tokens, ROI, and building platforms for AI-first teams (Part 2)]() Original publisher: [Read original article]() Author: Eira May Published: 2026-09-03T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI Bots](), [ai observability]() Tags: [agent](), [agentic](), [agentic-ai](), [agents](), [ai](), [ai-agents](), [business](), [cost](), [developers](), [engineering](), [engineering-leadership](), [google](), [infrastructure](), [leaders-of-code](), [model](), [observability](), [php](), [platform](), [platform-engineering](), [podcast](), [scale](), [science](), [tooling](), [work]() ### AI overview A podcast discussion on operating AI agents at scale, focusing on token efficiency, cost governance, context management, and platform tooling and observability. ### Source excerpt Andi Gutmans, head of Agentic Data Cloud at Google, returns for the second half of his Leaders of Code conversation to talk through the cost and infrastructure side of agentic development. ICYMI, part one covered judgment, code review, and data activation. ## Implementing the Anthropic AI-Native SDLC Playbook: How to get it right DevFeed: [Implementing the Anthropic AI-Native SDLC Playbook: How to get it right]() Original publisher: [Read original article]() Author: Yonatan Boguslavski Published: 2026-08-28T16:15:46Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [anthropic](), [sdlc](), [Platform Engineering](), [Agent Harness](), [Artificial Intelligence](), [AI-assisted coding]() Tags: [agentic](), [ai](), [anthropic](), [monitor](), [platform](), [platform-engineering](), [sdlc](), [tooling]() ### AI overview The article explains how to implement Anthropic's AI-native SDLC playbook at organizational scale. It describes agents contributing across the software lifecycle, producing artifacts from intent and specifications through plans, pull requests, and production, with governance, human judgment, orchestration, and monitoring built into a platform foundation. ### Source excerpt Implement Anthropic's AI-native SDLC playbook. Learn the key requirements and foundation needed to run agentic SDLC at scale. ## Introducing OttoFlow: AI Workflows for Kubernetes DevFeed: [Introducing OttoFlow: AI Workflows for Kubernetes]() Original publisher: [Read original article]() Author: Shreyas Mocherla Published: 2026-08-26T18:34:15Z Content type: release Language: en Sources: [Nirmata]() Topics: [Artificial Intelligence](), [Kubernetes](), [Platform Engineering](), [GitOps]() Tags: [agent](), [ai](), [ai-agents](), [ai-governance](), [declarative](), [gitops](), [kubernetes](), [kyverno](), [llm](), [nothing](), [platform-engineering](), [product](), [workflows]() ### AI overview Nirmata introduces OttoFlow, an open-source system for building AI workflows on Kubernetes. It represents workflows as custom resources with typed DAG steps and limits AI to selected parts of the operations process, aiming to combine deterministic, reviewable automation with model-based reasoning. ### Source excerpt AI workflows for Kubernetes, without handing an agent your kubeconfig. OttoFlow makes a workflow a custom resource: typed DAG steps, AI only where it counts. ## AI Software Factory: What It Is, Why You Need One, Who Owns It DevFeed: [AI Software Factory: What It Is, Why You Need One, Who Owns It]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-25T12:53:22Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [sdlc](), [Platform Engineering](), [DevOps]() Tags: [ai](), [devops](), [incident](), [platform-engineering](), [pull-requests](), [sdlc](), [testing]() ### AI overview The article argues that AI coding assistants alone do not improve end-to-end delivery because review, testing, and incident work can remain bottlenecks. It proposes an AI software factory that orchestrates agents, shared context, governance, and measurement across the SDLC. ### Source excerpt Discover what an AI software factory is, why it beats coding assistants, and who owns it in your engineering organization today. ## Internal Developer Platform Golden Paths Guide DevFeed: [Internal Developer Platform Golden Paths Guide]() Original publisher: [Read original article]() Author: Rashmi Hegde Published: 2026-08-25T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [internal developer platform](), [Platform Engineering](), [Developer Platform](), [Template]() Tags: [catalog](), [guide](), [internal-developer-platform](), [platform-engineering](), [productivity](), [self-service](), [visibility](), [workflows]() ### AI overview This guide explains how platform engineers can evolve an internal developer platform from a basic service catalog into maintained golden paths. It describes opinionated workflows, self-service capabilities, and embedded operational practices intended to improve developer adoption, speed, and reliability. ### Source excerpt Transform your service catalog into golden paths that developers love. Build an IDP that boosts productivity. Learn more. | Blog [Next page]()