# sdlc A methodology for designing, creating, and maintaining software. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How DHI Group accelerates generative AI workloads from idea to production using hackathons DevFeed: [How DHI Group accelerates generative AI workloads from idea to production using hackathons]() Original publisher: [Read original article]() Author: Umesh Kalaspurkar Published: 2026-09-17T15:13:42Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Artificial Intelligence](), [Generative AI](), [Hackathon](), [AI Development](), [Amazon Bedrock AgentCore](), [Amazon Web Services](), [Kiro](), [sdlc]() Tags: [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [customer-solutions](), [development](), [experimentation](), [generative-ai](), [hackathons](), [kiro](), [sdlc]() ### AI overview DHI Group partnered with AWS to use a structured Hackathon Acceleration Package to move generative AI workloads from experimentation toward production. The post describes preparation, enablement, and workshops covering Amazon Bedrock AgentCore, the AI-driven development lifecycle, and Kiro. ### Source excerpt Learn how DHI Group partnered with AWS to move generative AI workloads from idea to production using a structured hackathon. This post covers the Hackathon Acceleration Package, the winning ClearanceJobs and AgileATS agentic architecture on Amazon Bedrock AgentCore, and the principles that make hackathons a repeatable path to production. ## 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 ## 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. ## 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. ## 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. ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## 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. ## 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. ## 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 ## 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. ## 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. ## 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. ## The Agentic SDLC: The Software Lifecycle, Rebuilt Around Agents DevFeed: [The Agentic SDLC: The Software Lifecycle, Rebuilt Around Agents]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:42:24Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Agent](), [sdlc](), [AI Development](), [Platform Engineering](), [coding](), [Deployment](), [pull-requests]() Tags: [agentic](), [agents](), [coding](), [deployment](), [platform-engineering](), [pull-request](), [sdlc](), [testing]() ### AI overview This guide explains the agentic SDLC, a software development lifecycle in which AI agents lead work across planning, coding, testing, review, release, production operations, and incident response. Engineers define intent, review outcomes, and govern the process. It emphasizes that scaling from one agent to many requires context, human oversight, guardrails, visibility, and measurable ROI. ### Source excerpt Explore the Agentic SDLC, a software lifecycle rebuilt around smart agents to accelerate planning, coding, testing, and deployment. ## AI SDLC: A Practical Guide to SDLC AI Agents DevFeed: [AI SDLC: A Practical Guide to SDLC AI Agents]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:41:56Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Development](), [sdlc](), [Artificial Intelligence](), [ai-coding](), [coding](), [Software Engineering]() Tags: [ai](), [ai-agents](), [ai-coding](), [coding](), [developer](), [guide](), [sdlc](), [software-development](), [workflows]() ### AI overview A practical guide to using AI agents across the software development lifecycle. It explains how agents differ from coding assistants, describes an agentic SDLC in which agents perform multi-step engineering work while humans review and approve, and introduces organizational options for adopting it. ### Source excerpt Learn how AI agents support the SDLC, improve developer workflows, and help teams build, test, and ship software faster. ## What Are Agentic Workflows? A Guide for Platform Teams DevFeed: [What Are Agentic Workflows? A Guide for Platform Teams]() Original publisher: [Read original article]() Author: Aaron Taylor Published: 2026-08-10T11:41:40Z Content type: tutorial Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Development](), [sdlc](), [software-development](), [Artificial Intelligence](), [Software Engineering]() Tags: [agentic](), [ai](), [developers](), [development](), [engineering](), [guide](), [sdlc](), [software-development](), [workflows]() ### AI overview This guide explains agentic workflows as repeatable, multi-stage processes that use autonomous agents within platform-enforced guardrails across the AI software development lifecycle. It contrasts adaptive agent behavior with fixed scripts and describes how deterministic platform scaffolding can make non-deterministic reasoning safer and more reliable. ### Source excerpt Learn what agentic workflows are and how platform teams build them across the AI software development lifecycle. 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