# sdlc Published articles for sdlc. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Экосистема Digital Q от "Диасофт" вошла в число лидеров рейтингов CIO Navigator благодаря AI-driven подходу к разработке DevFeed: [Экосистема Digital Q от "Диасофт" вошла в число лидеров рейтингов CIO Navigator благодаря AI-driven подходу к разработке]() Original publisher: [Read original article]() Author: diasoft (Диасофт) Published: 2026-09-17T08:08:22Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Low code](), [Artificial Intelligence](), [Development](), [sdlc](), [Code]() Tags: [2026](), [agents](), [ai](), [ai-driven-ab7423f43dcb](), [development](), [digital-q](), [enterprise](), [low-code](), [sdlc](), [specification-driven-development](), [tag-077d33a42465](), [tag-2c039dce53be](), [tag-463bcbb8c0fe](), [tag-73efb20f7e33](), [tag-7b800b2da0b8]() ### AI overview Diasoft's Digital Q development ecosystem led the 2026 CIO Navigator ranking of Russian low-code solutions with AI features and placed second overall among 14 platforms. The article describes its AI-driven approach, including AI agents across the software development lifecycle and the use of machine-readable specifications to generate development artifacts. ### Source excerpt Компания "Диасофт" вошла в число лидеров сразу двух рейтингов российских low-code платформ 2026 года, опубликованных Санкт-Петербургским Клубом ИТ-директоров CIO Navigator. Экосистема разработки Digital Q возглавила рейтинг low-code решений с ИИ-функциями и заняла второе место в общем рейтинге российских low-code платформ. Лидерство экосистемы для разработчиков Digital Q в рейтинге российских low-code платформ с функциями ИИ стало возможным по мнению организаторов рейтинга благодаря AI-driven подходу, при котором искусственный интеллект используется на всех этапах создания и развития программного обеспечения. Участников исследования оценивали по более чем 170 критериям, охватывающим возможности искусственного интеллекта, архитектуру, инструменты разработки и другие характеристики, значимые для корпоративного применения. В общем рейтинге российских low-code платформ Digital Q заняла второе место среди 14 представленных решений. Исследование включало более 180 критериев - по функциональности, архитектуре, безопасности, интеграционным возможностям, инструментам управления жизненным циклом разработки и ИИ-функциям. CIO Navigator характеризует Digital Q как корпоративную low-code экосистему для создания и развития микросервисных информационных систем уровня enterprise, которая развивается в направлении AI-driven платформы для управляемой ИИ-разработки. В основе развития Digital Q лежит переход от использования ИИ как отдельного помощника разработчика к модели AI-Native SDLC, в которой искусственный интеллект становится полноценным участником жизненного цикла создания программного обеспечения. ИИ-агенты включаются в работу с требованиями, проектирование, разработку, тестирование и последующее сопровождение решений. Для их оркестрации в экосистеме используется платформа Digital Q.Agents. Именно сквозное применение ИИ на протяжении всего цикла разработки CIO Navigator выделяет как одно из ключевых отличий Digital Q. Читать далее ## Platform Engineering in the Age of AI DevFeed: [Platform Engineering in the Age of AI]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI Agent](), [internal developer portal](), [API](), [Deployment](), [sdlc]() Tags: [agent](), [ai](), [api](), [deployment](), [developer-experience](), [engineering](), [metrics](), [platform](), [sdlc]() ### AI overview This commentary examines how platform engineering is adapting as AI coding tools and agents become part of software delivery. It discusses the build-versus-provide decisions facing platform teams, how agents may consume internal developer platforms through APIs, the need for guardrails, and the challenge of measuring the results of AI investment. ### Source excerpt 94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. | Blog ## Claude Managed Agents: How They Work and Where They Fit DevFeed: [Claude Managed Agents: How They Work and Where They Fit]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-09-14T11:29:41Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [Claude](), [Artificial Intelligence](), [sdlc](), [context window](), [Network](), [Processes]() Tags: [agent](), [agents](), [ai](), [claude](), [context-window](), [infrastructure](), [network](), [platform](), [sandbox](), [sdlc](), [software](), [tools]() ### AI overview This article explains Claude Managed Agents, a hosted runtime and managed agent harness operated by Anthropic. It describes how the harness coordinates tools and execution, the sandbox provides isolated command and file access, and the session preserves durable task history outside the model's context window. It also discusses the additional platform layer needed to connect multiple agents across a business process and the SDLC. ### Source excerpt What Claude Managed Agents are, how the runtime works, and how platform teams connect several agents across the SDLC. ## 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. ## What does DX do? Key features, use cases, and alternatives DevFeed: [What does DX do? Key features, use cases, and alternatives]() Original publisher: [Read original article]() Author: John Crowley Published: 2026-09-09T15:53:52Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [code productivity](), [developer-productivity](), [Developer experience](), [atlassian](), [Git](), [Instrumentation](), [sdlc](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [atlassian](), [developer-experience](), [developer-productivity](), [engineering](), [git](), [instrumentation](), [metrics](), [sdlc](), [survey]() ### AI overview This article explains what DX does as an engineering intelligence platform. DX measures developer productivity, developer experience, delivery data, and AI's impact on codebases using developer surveys and data from Git and Jira. It describes DX's DevEx Cloud, DX Data Cloud, DX AI, and Fabric offerings, along with their research-backed metrics, use cases, limitations, and alternatives. ### Source excerpt What DX does: its engineering intelligence features, survey-based metrics, common use cases, where it stops, and the alternatives ## Making Operational Readiness Testing Continuous with Harness DevFeed: [Making Operational Readiness Testing Continuous with Harness]() Original publisher: [Read original article]() Author: Uma Mukkara Published: 2026-09-08T17:57:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Testing](), [Resilience](), [sdlc](), [Deployment](), [configuration]() Tags: [configuration](), [deployment](), [harness](), [resilience](), [sdlc](), [testing]() ### AI overview The article argues that Operational Readiness Testing should be continuous rather than a one-time pre-release checklist. It describes how Harness Resilience Testing can apply ongoing checks to services as dependencies, configuration, deployments, and operating conditions change. ### Source excerpt Harness makes Operational Readiness Testing continuous, applying resilience checks to every SDLC change. | Blog ## Scaling your money safely with AI DevFeed: [Scaling your money safely with AI]() Original publisher: [Read original article]() Published: 2026-09-08T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI-assisted coding](), [sdlc](), [Code]() Tags: [agentic-ai](), [ai](), [ai-coding](), [autonomous](), [loops](), [podcast](), [sdlc](), [se-stackoverflow](), [se-tech](), [security]() ### AI overview A podcast conversation about validating AI-generated code for security, building autonomous SDLC harnesses with feedback loops, and creating a headless checkout experience. ### Source excerpt Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience. ## Agentic Software Engineering Platform: Definition, Capabilities DevFeed: [Agentic Software Engineering Platform: Definition, Capabilities]() Original publisher: [Read original article]() Author: Aaron Taylor Published: 2026-09-07T12:48:46Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Software Engineering](), [AI Development](), [sdlc](), [Orchestration](), [Model Context Protocol](), [CI/CD](), [real-time](), [Slack]() Tags: [agentic](), [ai](), [ai-agents](), [ci-cd](), [mcp](), [orchestration](), [platforms](), [sdlc]() ### AI overview The article defines an Agentic Software Engineering Platform as infrastructure that enables AI agents and developers to work safely across the software development lifecycle. It describes a unified engineering context layer, workflow orchestration, governance, MCP connections, CI/CD integration, and ways to demonstrate return on investment. ### Source excerpt Learn what an Agentic Software Engineering Platform is, its five core capabilities, when to adopt one, and how to measure its ROI. ## How Every Team Can Build in Port at Scale, Without the Bottleneck DevFeed: [How Every Team Can Build in Port at Scale, Without the Bottleneck]() Original publisher: [Read original article]() Author: Guy Berman Published: 2026-09-03T14:39:07Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Authorization](), [Scalability](), [sdlc](), [pull-requests](), [Security]() Tags: [agent](), [agentic](), [compliance](), [dashboards](), [development](), [production](), [pull-requests](), [scalability](), [scale](), [sdlc](), [security](), [workflow]() ### AI overview The article explains how every team can build and own a scoped part of Port at scale. It presents fine-grained permissions, role-based access controls, and controlled promotion across development, staging, and production environments as ways to reduce platform-team bottlenecks while limiting risk. ### Source excerpt How every team can build in Port at scale: scoped permissions, dynamic policies, and safe rollouts across environments. ## 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 ## QE Platform Strategy: 3 Paths and Where Each Hides Its Cost DevFeed: [QE Platform Strategy: 3 Paths and Where Each Hides Its Cost]() Original publisher: [Read original article]() Author: Rick Crawford Published: 2026-08-31T17:37:05Z Content type: opinion Language: en Sources: [Postman Blog]() Topics: [Testing](), [Software Engineering](), [Development](), [Artificial Intelligence](), [Open Source]() Tags: [ai](), [api-testing](), [blog](), [development](), [general](), [open-source](), [qe-program](), [quality-engineering](), [sdlc](), [software-engineering](), [strategy](), [testing]() ### AI overview This opinion article compares three QE platform strategies: continuing with the current approach, building an internal platform, or integrating specialized tools. It explains the hidden costs of each path, including test maintenance, delayed releases, escaped defects, long-term platform maintenance, dependency updates, security patches, and the need to modernize for AI-assisted testing. ### Source excerpt In Part 1 I named the six structural problems compounding inside QE programs as AI accelerates development. In Part 2 I walked... The post QE Platform Strategy: 3 Paths and Where Each Hides Its Cost appeared first on Postman Blog. ## 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. ## Auditing Coding-Agent Configuration, Skills, Memory, and Hooks DevFeed: [Auditing Coding-Agent Configuration, Skills, Memory, and Hooks]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-27T14:31:10Z Content type: tutorial Language: en Sources: [Elevate]() Topics: [Agent Skill](), [configuration](), [AI-assisted coding](), [Claude](), [sdlc]() Tags: [agent](), [agent-skill](), [agent-skills](), [claude](), [coding-agents](), [configuration](), [markdown](), [sdlc]() ### AI overview This practical guide argues that coding-agent configuration files can become outdated as models, harnesses, and codebases change. It recommends periodically reviewing configuration, memory, skills, and hooks, while acknowledging concerns about token costs, excessive instructions, and uncertain effects on quality. The author describes using Claude's /doctor and maintaining Agent Skill packages. ### Source excerpt A practical guide to auditing what your coding agent still needs. ## 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. ## Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain DevFeed: [Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain]() Original publisher: [Read original article]() Author: Yaron Avital Published: 2026-08-21T23:00:21Z Content type: article Language: en Sources: [Unit 42]() Topics: [sdlc](), [Security](), [Application Security](), [CI/CD](), [GitHub Actions](), [npm](), [Bun](), [Python](), [Claude Code](), [vs-code](), [Claude](), [Ethereum]() Tags: [blockchain](), [c2](), [chaindrop](), [ci-cd](), [claude-code](), [general](), [github-actions](), [insights](), [malware](), [npm-packages](), [persistence](), [python](), [sdlc](), [security](), [software-supply-chain-attack](), [supply-chain](), [vs-code]() ### AI overview Unit 42 describes how software supply-chain attackers are targeting developer tools, CI/CD pipelines, accounts, packages, setup scripts and developer environments before software reaches production. It examines the ChainDrop npm worm, which used malicious preinstall hooks, a Bun runtime, an obfuscated payload, Python-based memory theft, stolen tokens and secrets, self-propagation, persistence in VS Code and Claude Code, and Ethereum-managed command-and-control infrastructure. ### Source excerpt Attackers are targeting CI/CD pipelines and developer tools instead of application code, requiring total SDLC visibility and strict security controls The post Connecting the Dots: Securing the Overlooked Corners of the Software Development Lifecycle (SDLC) Supply Chain appeared first on Unit 42. ## 5 QE Pipeline Metrics That Show Where Quality Is Leaking DevFeed: [5 QE Pipeline Metrics That Show Where Quality Is Leaking]() Original publisher: [Read original article]() Author: Rick Crawford Published: 2026-08-21T15:00:10Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Development](), [Postman]() Tags: [api-testing](), [developer](), [development](), [false-positives](), [general](), [metrics](), [packages](), [policy](), [production](), [qe-program](), [quality-engineering](), [sdlc](), [shift-left](), [validation]() ### AI overview This developer article presents five quality-engineering pipeline metrics aligned with Design, Gate, Validate, Monitor, and Improve stages. The supplied text details package adoption, gate coverage and flake rate, and defect escape rate, explaining how they reveal uneven coverage, ineffective merge gates, false positives, and defects reaching production. ### Source excerpt In the last post I described six problems that compound when development outruns the QE infrastructure underneath it. Most leaders recognize all... The post 5 QE Pipeline Metrics That Show Where Quality Is Leaking appeared first on Postman Blog. ## Product Release Notes - July 2026 DevFeed: [Product Release Notes - July 2026]() Original publisher: [Read original article]() Author: Matan Grady Published: 2026-08-20T08:13:19Z Content type: release Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Agent](), [Artificial Intelligence](), [Claude](), [anthropic](), [dashboards](), [Model Context Protocol](), [sdlc](), [Single sign-on (SSO)](), [pull-requests]() Tags: [2026](), [agent](), [agentic](), [agents](), [ai](), [ai-agent](), [anthropic](), [claude](), [dashboards](), [events](), [integration](), [integrations](), [mcp](), [product](), [product-release](), [pull-requests](), [release](), [release-notes](), [sdlc](), [workflows]() ### AI overview Port's July 2026 release notes describe expanded Port AI capabilities for planning and executing approved platform changes. The release adds agentic catalog actions, Ask, Plan, and Build modes, dashboard creation tools, inline integration setup, structured clarifying questions, visible reasoning, Claude Managed Agents integration, conversational onboarding, Survey Intelligence, and end-to-end SSO administration. ### Source excerpt July brings Port AI's biggest step yet toward taking action on your behalf -- from an AI Agent that can create blueprints and trigger workflows, to AI-guided conversational onboarding that sets up new orgs automatically, plus a brand new way to capture team sentiment with Survey Intelligence. Here's everything that's new in Port this month. ## What Does Xirp Do? Key Features, Use Cases, and Alternatives DevFeed: [What Does Xirp Do? Key Features, Use Cases, and Alternatives]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-18T14:34:23Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI-assisted coding](), [codex](), [Claude Code](), [macOS](), [internal developer portal](), [Backstage](), [MCP](), [sdlc](), [Command-line interface](), [Code]() Tags: [agentic](), [ai-coding-agents](), [backstage](), [claude-code](), [codex](), [coding-agents](), [gemini](), [macos](), [mcp](), [sdlc](), [software-catalog]() ### AI overview Xirp is Spotify's macOS desktop app for running Claude Code, Codex, and Gemini in parallel persistent terminal sessions. It provides isolated git worktrees, preserves session context when switching agents, and can connect to Spotify Portal so agents receive software catalog context through MCP. ### Source excerpt Xirp is Spotify's app for running AI coding agents in parallel sessions. What it does, when to use it, and the main alternatives. ## Building an agentic SDLC with a QA engineering mindset DevFeed: [Building an agentic SDLC with a QA engineering mindset]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-08-18T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [sdlc](), [ai observability](), [AI-assisted coding]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [architecture](), [automation](), [design](), [engineering](), [evaluation](), [llms](), [observability](), [podcast](), [sdlc](), [se-stackoverflow](), [se-tech]() ### AI overview A discussion of end-to-end agentic SDLC pipelines, QA-led specification enrichment, and evaluation of multiple LLMs-as-judges using Cohen's kappa. It also references cross-layer observability for LLM-assisted test automation. ### Source excerpt Ryan welcomes Suneet Malhotra, Senior Manager of Test Engineering at Motorola Solutions, to chat about building end-to-end agentic SDLC pipelines using MCPs, using Cohen's kappa to evaluate multiple LLMs-as-judges, and how you can improve requirements by shifting QA left through a specification enrichment stage immediately after the design phase. ## How to move fast toward the right thing DevFeed: [How to move fast toward the right thing]() Original publisher: [Read original article]() Author: Jake Albaugh Published: 2026-08-13T20:30:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Tech Debt](), [Large Language Model](), [Vibe coding](), [agentic-coding](), [sdlc]() Tags: [agentic-coding](), [ai](), [developers](), [llms](), [sdlc](), [tech-debt](), [vibe-coding]() ### AI overview AI makes software building faster and more accessible, but can also produce tech debt and encourage uncritical acceptance of polished outputs. The article argues that teams should begin with clear intent, carefully decide what is worth building, and use AI to translate that intent into software while retaining human judgment. ### Source excerpt With AI, speed comes easy, but so does tech debt. That's why the best teams don't just ship something fast; they consider it carefully, build it efficiently, and make it stand out. ## How Platform Engineers Enforce Engineering Standards With AI DevFeed: [How Platform Engineers Enforce Engineering Standards With AI]() Original publisher: [Read original article]() Author: Etay Alony Published: 2026-08-11T12:00:07Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Agent](), [Platform Engineering](), [Pull Request](), [sdlc](), [changelog]() Tags: [agentic](), [agents](), [ai](), [ai-agent](), [ai-agents](), [building](), [coding-agents](), [developer](), [engineering](), [platform](), [platform-engineering](), [pull-request](), [review](), [scale](), [sdlc]() ### AI overview The article explains how platform engineering teams can use AI agents to enforce engineering standards across many repositories. Agents inspect scorecards, identify noncompliant services, implement fixes, and open pull requests for service owners to review, allowing platform engineers to focus on exceptions and governance. It also introduces five practices for scaling this approach safely, including providing agents with accurate organizational context and safeguards. ### Source excerpt How platform teams use AI agents to enforce engineering standards across hundreds of repos, and the five foundations it needs. ## How HoneyBook uses Port to serve engineering & agents across the AI SDLC? DevFeed: [How HoneyBook uses Port to serve engineering & agents across the AI SDLC?]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T14:26:14Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [sdlc](), [DevOps](), [Deployment](), [Automation](), [GitHub](), [Tooling]() Tags: [agents](), [ai-agents](), [automation](), [deployment](), [developers](), [devops](), [engineering](), [github](), [platform](), [production](), [sdlc](), [workflows]() ### AI overview HoneyBook moved deployment, rollback, and new-service creation workflows onto Port so developers could perform routine platform tasks themselves. The self-service flows expose service state, Argo CD health, production commits, logs, and rollback actions while enforcing deployment safeguards such as end-to-end test completion and manager approval for bypasses. HoneyBook is also extending these workflows to AI agents. ### Source excerpt See how HoneyBook turned deploys, rollbacks, and new-service creation into self-service flows with Port - and is extending them to AI agents. ## Practical AI in Platform Engineering: lessons from Port's latest meetup DevFeed: [Practical AI in Platform Engineering: lessons from Port's latest meetup]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:42:45Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI-assisted coding](), [Code review](), [sdlc](), [Code generation](), [Incident response](), [Infrastructure as code](), [Security](), [vulnerability]() Tags: [agentic](), [ai](), [ai-coding](), [engineering](), [iac](), [ide](), [incident-response](), [platform-engineering](), [review](), [sdlc](), [security](), [testing]() ### AI overview Port's Tel Aviv meetup examined how engineering organizations are adopting AI in platform engineering. Speakers discussed agentic workflows for bug triage, platform requests, and the SDLC, while an anonymous survey measured AI maturity, production deployments, evaluation practices, human oversight, and concerns about autonomy. Most teams reported having a few SDLC agents but not scaling them broadly; code review and code generation were the most common production uses, while operational applications such as infrastructure, security, and incident response lagged behind. ### Source excerpt We hosted a meetup with engineering leaders working on AI inside their orgs and ran an anonymous survey. Here's what came out of it. ## Agentic SDLC in Practice: Insights from Engineering Leaders DevFeed: [Agentic SDLC in Practice: Insights from Engineering Leaders]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:42:34Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [sdlc](), [Artificial Intelligence](), [AI Agent](), [Platform Engineering](), [Model Context Protocol](), [MCP Server](), [incident](), [Data Quality](), [Amazon Bedrock](), [Slack](), [cursor](), [Pull Request]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-agent](), [bedrock](), [coding](), [cursor](), [data-quality](), [engineering](), [incident](), [mcp](), [mcp-server](), [platform-engineering](), [python](), [review](), [sdlc](), [server](), [slack](), [workflow]() ### AI overview Engineering leaders share practical examples of an agentic SDLC, including autonomous Jira ticket resolution through pull request creation and AI-powered incident triage. The article highlights adoption challenges such as siloed agents, missing governance, and the need for a shared registry of agents, skills, and MCPs. ### Source excerpt Engineering leaders share how agentic SDLC works in practice: what scales adoption, where teams get stuck, and how to maximize AI ROI. [Next page]()