# software delivery Published articles for software delivery. 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 to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## 1Password increases engineering productivity 21% with Codex DevFeed: [1Password increases engineering productivity 21% with Codex]() Original publisher: [Read original article]() Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding]() Tags: [codex](), [engineering](), [features](), [internal-tools](), [productivity](), [pull-request](), [security](), [site-reliability-engineering](), [software-delivery](), [user-stories](), [writing-code]() ### AI overview 1Password reports using Codex across its software delivery lifecycle to turn user stories into near-final prototypes and features more quickly. It cites improved engineering productivity and shorter pull request cycle times while maintaining security standards. ### Source excerpt Engineers at 1Password use Codex to rapidly build new features and internal tools, reaching production-readiness while maintaining rigorous security policies. ## 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 ## Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration DevFeed: [Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration]() Original publisher: [Read original article]() Author: Eric Metaj; Mark Azer Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Developer Tools](), [iac-security](), [Vulnerabilities]() Tags: [apm](), [bitbucket](), [ci-visibility](), [cloud](), [code](), [code-profiling](), [code-security](), [devsecops](), [error-tracking](), [infrastructure-as-code](), [integration](), [observability](), [open-source](), [pull-requests](), [secret-scanning](), [security](), [software-delivery](), [test-optimization](), [vulnerabilities]() ### AI overview Datadog's Bitbucket Cloud Source Code integration links selected repositories with Datadog so teams can investigate production issues with source context and review security findings before merging code. ### Source excerpt Connect Bitbucket Cloud to Datadog to troubleshoot with source code in context and surface test, quality, and security feedback in pull requests. ## GitLab compliance frameworks: Adhere to SOC 2 in minutes DevFeed: [GitLab compliance frameworks: Adhere to SOC 2 in minutes]() Original publisher: [Read original article]() Author: Fernando Diaz Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [GitLab](), [Template](), [configuration](), [Security](), [Software]() Tags: [application-security](), [article](), [compliance](), [devsecops](), [framework](), [how-to](), [platform](), [security](), [soc](), [software](), [software-delivery](), [tutorial]() ### AI overview This article explains how GitLab custom compliance framework templates help teams define controls, continuously monitor adherence, and prepare for audits. It uses SOC 2 as a central example and covers templates, policies, compliance requirements, and automated checks across GitLab deployment options. ### Source excerpt Compliance is the part of software delivery that everyone agrees is important, yet nobody enjoys. It often lives in spreadsheets, screenshots, and the quiet dread of an upcoming audit. GitLab's custom compliance frameworks work differently. Instead of documenting what should be true about your projects, you define the controls once and let the platform continuously verify what is true. In this article, you'll learn why compliance adherence matters, how to stand up a SOC 2 framework in minutes using a template, how to monitor adherence on an ongoing basis, how to enforce compliance with policies, and which standards already ship as ready-to-use templates. You'll also get a look at where AI-specific compliance templates are heading. You can watch the video below to see it in action: Tier note: Compliance frameworks are available in Premium and Ultimate. Framework requirements and controls, the checks that power adherence reporting and the compliance status report, require Ultimate. Everything here applies to GitLab.com, GitLab Self-Managed, and GitLab Dedicated. Why compliance adherence matters and how custom frameworks help Regulatory and contractual obligations like SOC 2, ISO 27001, PCI DSS, and FedRAMP exist because customers, partners, and regulators need assurance that your software is built and shipped responsibly. Falling short isn't just a paperwork problem. It can block deals, trigger fines, delay product launches, and erode trust. Compliance also isn't a one-time event. A project that was compliant at audit time can drift the moment someone disables a scanner, removes branch protection, or merges without the required approvals. Custom compliance frameworks are designed to close that gap. A compliance framework in GitLab is a label you create on a top-level group to identify projects that carry specific compliance obligations or need extra oversight. On its own that label provides organization and visibility. In Ultimate, a framework can also carry requireme ## Introducing Agent-Ready Code Repository & AI Code Review DevFeed: [Introducing Agent-Ready Code Repository & AI Code Review]() Original publisher: [Read original article]() Author: Juveria Kanodia Colin Chartier Published: 2026-08-27T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [Pull Request](), [Code review]() Tags: [ai](), [ai-agents](), [ai-code-review](), [code-generation](), [code-review](), [software-delivery]() ### AI overview Harness introduces an agent-ready Code Repository and built-in AI Code Review to manage AI-generated code at high volume. The capabilities use risk-based diffs and scoped agent permissions to support governed software delivery before code merges. ### Source excerpt Legacy SCMs can't handle agent-scale code volume. Learn how Harness Code Repository and built-in AI Code Review handle AI-generated code at scale using risk-bas | Blog ## Scale software delivery pipelines in isolation without owning the runner fleet DevFeed: [Scale software delivery pipelines in isolation without owning the runner fleet]() Original publisher: [Read original article]() Author: Kyurim Rhee Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [ci](), [GitLab](), [Software as a service](), [Security](), [cloud-infrastructure](), [Provisioning]() Tags: [agentic](), [ci](), [compliance](), [cost](), [developers](), [devsecops-platform](), [infrastructure](), [operations](), [platform](), [product](), [saas](), [scale](), [security](), [software-delivery](), [workflows]() ### AI overview GitLab presents Hosted Runners for GitLab Dedicated, a fully managed CI execution SaaS offering with isolated runner infrastructure. Each job runs in a newly provisioned VM that is deleted after completion, helping enterprises address security, compliance, scalability, reliability, and runner-operations demands without owning the runner fleet. ### Source excerpt Many enterprises choose GitLab Dedicated for a clear reason: a secure and compliant, single-tenant GitLab instance, managed by GitLab. As agentic workflows drive more pipeline volume, full data isolation and runner infrastructure operational burden become a challenge. This leads to the question, "Does owning the runner fleet still make sense?" With GitLab Dedicated, there is no more need to provision, patch, build, and scale your own runner fleet. Hosted Runners for GitLab Dedicated handles the heavy lifting and delivers a seamless experience across the entire software development lifecycle so your team can focus on shipping. Hosted Runners for GitLab Dedicated provides a runner fleet isolated from other customers. To support job-level security, each job runs in a newly provisioned, isolated VM that is deleted after the job completes. We provide a scalable and highly reliable runner fleet to support your bursty CI demands and resiliency needs. Creating and managing runners is self-serviceable through Switchboard (GitLab Dedicated management console). The runner ownership trade-off: Cost vs. user experience Pipeline load can be unpredictable. To account for this, runner infrastructure can be over-provisioned to give developers the best experience and maintain low wait times. But this drives up the infrastructure cost. Conversely, under-provisioning can save on infrastructure cost, but your developers will waste time waiting for their CI jobs to be picked up. Efforts to fine-tune this balance can monopolize your platform team's time. Shift runner operations to GitLab while maintaining control Hosted Runners for GitLab Dedicated is a fully managed CI execution SaaS offering. It's backed by a high-performing and reliable runner infrastructure. Key benefits include: Supports compliance: Full isolation/single-tenant runners with same data residency as GitLab Dedicated. Job level security: "One runner for one job" -- hosted runners run every job in a standalone, short-lived ## Run agentic software delivery inside the boundaries you already trust DevFeed: [Run agentic software delivery inside the boundaries you already trust]() Original publisher: [Read original article]() Author: Ozer Dondurmacioglu Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [GitHub](), [Amazon Bedrock](), [Cloud](), [Amazon Web Services](), [Inference](), [Software as a service](), [AI Development](), [Critical Infrastructure]() Tags: [agentic](), [ai](), [amazon-bedrock](), [aws](), [cloud](), [critical-infrastructure](), [inference](), [product](), [software](), [software-delivery](), [software-development]() ### AI overview GitLab introduces AI Gateway for GitLab Dedicated, enabling customers to run AI processing within their single-tenant environment and selected AWS region. The gateway supports Amazon Bedrock and other preferred model providers, while GitLab maintains the managed infrastructure, availability, and disaster recovery. GitLab Duo Agent Platform capabilities such as code review automation, failed-pipeline fixes, and security analysis can use customer-selected models. ### Source excerpt Many enterprises choose GitLab Dedicated for a clear reason: a single-tenant instance, managed by GitLab, in a cloud region they select. That isolation already covers source code, project data, and the software development lifecycle they run on GitLab. GitLab Dedicated customers can now deploy the AI Gateway for the GitLab Duo Agent Platform inside their single-tenant SaaS infrastructure, so AI-processed data stays in their environment and chosen region. GitLab Dedicated as the critical infrastructure for software delivery GitLab Dedicated is deployed in your preferred AWS region, and hosted and maintained by GitLab. You can bring your own encryption keys. No private cloud installation required. GitLab operates the instance, including high availability and disaster recovery, and maintains 99.9% monthly availability for core services. Updates to GitLab Dedicated follow a stability-first cadence, with instances on the previous minor version (N-1) relative to the current GitLab release. That is the infrastructure organizations already trust when they scale software delivery under audit: guardrails you can take to a regulator, SaaS operations you do not staff, and an availability target that holds when the platform is in the critical path. Trust should travel with every agent Agentic workflows demand more from your software development lifecycle beyond the AI coding: increased pipeline volume, greater risk for vulnerabilities introduced, context that needs to travel with every agent and flow, among others. The isolation, residency, and service-level requirements that already apply to GitLab Dedicated should apply to that inference path too, especially with growing adoption of agentic software development. Adopt AI while staying compliant Here is what's new with the introduction of AI Gateway for GitLab Dedicated: Keep AI processing in the tenancy. The AI Gateway runs in the Dedicated single-tenant environment, aligned with data residency. Bring your own models. Connect ## Harness Community: Connect, Learn, and Build Together DevFeed: [Harness Community: Connect, Learn, and Build Together]() Original publisher: [Read original article]() Author: Dewan Ahmed Ankit Kumar Published: 2026-08-12T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Software](), [CI/CD]() Tags: [blog](), [community](), [harness](), [software-delivery]() ### AI overview Harness announces the launch of the Harness Community, an open discussion forum where users can connect with practitioners, share expertise, solve delivery challenges, and help shape the platform's future. ### Source excerpt Join the Harness Community to connect with practitioners, solve delivery challenges, share expertise, and shape the future of software delivery. | Blog ## Software Delivery Platform: Key Features & How to Evaluate DevFeed: [Software Delivery Platform: Key Features & How to Evaluate]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Software](), [CI/CD](), [Continuous Delivery (CD)](), [Continuous integration]() Tags: [audit](), [automation](), [blog](), [ci-cd](), [ci-cd-pipeline](), [governance](), [how-to](), [progressive-delivery](), [rollback](), [scalability](), [security](), [software-delivery](), [visibility]() ### AI overview This article explains how a software delivery platform differs from a standalone CI/CD tool. It describes a platform as an integrated system covering the full after-code lifecycle, including continuous integration, continuous delivery, security, governance, insights, coordination, and rollback, then outlines criteria for evaluating vendors. ### Source excerpt A software delivery platform isn't just a CI/CD tool. Get the must-have feature checklist and the demo questions to use when evaluating vendors. | Blog ## Agentic AI Development Tools: How to Build Your Stack DevFeed: [Agentic AI Development Tools: How to Build Your Stack]() Original publisher: [Read original article]() Author: Aaron Taylor Published: 2026-08-10T11:34:27Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Development](), [developer tooling](), [AI-assisted coding](), [sdlc](), [CI/CD](), [observability]() Tags: [agentic-ai](), [agents](), [ai-agents](), [ai-development](), [ci-cd](), [code-generation](), [development-tools](), [observability](), [sdlc](), [software-delivery]() ### AI overview This guide explains how to choose and connect agentic AI development tools across the software development lifecycle. It distinguishes the agent layer, the agentic infrastructure layer, and the existing foundation of source control, CI/CD, and observability tools, then introduces criteria for evaluating tools and begins with code generation agents. ### Source excerpt Explore agentic AI development tools and learn how to build a practical stack for faster, more reliable AI software delivery. ## How NIIT helped Kyndryl certify more than 100 platform engineers DevFeed: [How NIIT helped Kyndryl certify more than 100 platform engineers]() Original publisher: [Read original article]() Author: Karolina Semiuk Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer portal](), [Developer experience](), [Scalability](), [DevOps]() Tags: [best-practices](), [certifications](), [community](), [devops](), [efficiency](), [engineering](), [enterprise](), [organizational](), [platform](), [scalability](), [software-delivery]() ### AI overview The article describes how NIIT helped Kyndryl certify more than 100 platform engineers. It argues that team-scale certification creates shared language, reduces communication problems, standardizes best practices, and strengthens platform engineering capabilities across a distributed enterprise workforce. ### Source excerpt NIIT enabled Kyndryl to certify 100+ platform engineers, fostering shared language and organizational transformation through team-based certification programs ## DevOps Technologies in 2026: What Actually Matters DevFeed: [DevOps Technologies in 2026: What Actually Matters]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-22T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Software](), [Artificial Intelligence](), [ai-coding](), [GitHub Actions]() Tags: [ai-coding](), [ai-tools](), [automation](), [devops](), [fragmentation](), [governance](), [observability](), [software-delivery]() ### AI overview This article surveys DevOps technologies in 2026, describing a shift from specialized point tools toward unified platforms spanning software delivery, infrastructure, security, and observability. It argues that automation, observability, and governance are central priorities, and discusses how AI coding tools are changing delivery risks and practices. ### Source excerpt Explore the DevOps technologies transforming software delivery in 2026. Learn which tools matter, how AI is changing the game, and what to focus on next. | Blog ## DevOps Toolchain: Build a Stack That Actually Scales DevFeed: [DevOps Toolchain: Build a Stack That Actually Scales]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-22T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [toolchain](), [CI/CD](), [ai-coding]() Tags: [ai-coding](), [ci-cd](), [devops](), [productivity](), [software-delivery](), [toolchain]() ### AI overview This article explains that a scalable DevOps toolchain should be a small, unified stack with shared governance and golden paths rather than a large collection of specialized tools. It argues that tool sprawl increases integration work, governance gaps, context switching, and rollback complexity, especially as AI increases code volume. ### Source excerpt Discover the essential DevOps toolchain for modern software delivery. Learn why consolidation, not sprawl, is how teams scale safely with AI acceleration. | Blog ## Why Database DevOps Matters in Modern Software Delivery DevFeed: [Why Database DevOps Matters in Modern Software Delivery]() Original publisher: [Read original article]() Author: Jesse Wang Published: 2026-07-17T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Databases](), [Infrastructure as code](), [DevOps](), [Database Migration](), [iac-security](), [Provisioning](), [Platform Engineering](), [Terraform](), [AWS CloudFormation]() Tags: [aws-cloudformation](), [database](), [deployment](), [devops](), [infrastructure](), [infrastructure-as-code](), [migrations](), [platform-engineering](), [software-delivery](), [terraform]() ### AI overview The article explains that Infrastructure as Code improved infrastructure provisioning but did not address database schema change delivery. It argues that separating infrastructure, application, and database workflows creates operational risk, and presents Database DevOps as a way to bring version control, rollback, and governance to database delivery. ### Source excerpt Infrastructure as Code transformed infrastructure delivery. Learn why database delivery requires a different approach and how Database DevOps closes the gap. | Blog ## DevOps Tools by Software Delivery Stage: Building a Unified Stack in 2026 DevFeed: [DevOps Tools by Software Delivery Stage: Building a Unified Stack in 2026]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [Software](), [Deployment](), [Security]() Tags: [2026](), [ai](), [devops](), [software-delivery]() ### AI overview This article explains how to organize DevOps tools across the software delivery lifecycle, from source control and build through testing, security, deployment, and operations. It argues that a smaller, integrated stack with shared governance, audit trails, data, and golden paths is more useful than a long inventory of disconnected tools. ### Source excerpt A 2026 DevOps tools list by stage: the categories you actually need, why consolidation beats sprawl, and which best DevOps tools scale with AI acceleration. | Blog ## DevOps Solutions: How to Pick the Right Stack in 2026 DevFeed: [DevOps Solutions: How to Pick the Right Stack in 2026]() Original publisher: [Read original article]() Author: Eric Minick Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [DevOps](), [sdlc](), [CI/CD](), [Tooling](), [Developer experience](), [observability](), [Security](), [Automation](), [Scalability](), [Architecture & Design]() Tags: [ci](), [ci-cd](), [developer-experience](), [devops](), [observability](), [platforms](), [scalability](), [security](), [software-delivery](), [tooling]() ### AI overview This guide explains how to evaluate DevOps solutions and choose between point tools and integrated platforms. It focuses on software delivery capabilities, integration, governance, scalability, AI capability, developer experience, and total cost of ownership. ### Source excerpt Looking for the right DevOps solutions for your team? Compare top DevOps software and platforms to build a software delivery stack that scales. | Blog ## 5 takeaways from the State of Software Delivery Q2 Pulse report DevFeed: [5 takeaways from the State of Software Delivery Q2 Pulse report]() Original publisher: [Read original article]() Author: Jacob Schmitt Published: 2026-07-08T13:00:00Z Content type: release Language: en Sources: [The CircleCI Blog Feed | CircleCI]() Topics: [Software](), [Development](), [Artificial Intelligence]() Tags: [ci-cd-benchmarks](), [circleci-news](), [cost](), [developer-productivity](), [efficiency](), [merge-efficiency-ratio](), [popular](), [report](), [software-delivery](), [software-delivery-metrics](), [software-delivery-pulse](), [software-delivery-report](), [state-of-software-delivery](), [teams](), [velocity](), [workflows]() ### AI overview CircleCI releases its 2026 State of Software Delivery Q2 Pulse report, based on more than 20 million workflows analyzed in March 2026. The report finds that the gap between high-performing teams and the median team widened, while feature-branch throughput increased and main-branch throughput remained flat. ### Source excerpt The velocity gap is widening. See what 20M+ workflows reveal about merge efficiency, delivery costs, and the teams pulling ahead in the Q2 Pulse report. ## Three internal tools Datadog built with AI that improved developer experience and system performance DevFeed: [Three internal tools Datadog built with AI that improved developer experience and system performance]() Original publisher: [Read original article]() Author: Joe McGovern; Alex Zvorygin; Alex Bianchi; Colin Nichols Published: 2026-07-01T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [developer tooling](), [Deployment Tools](), [Orchestration](), [AI Bots](), [SDKs](), [sdlc]() Tags: [ai](), [bits-ai](), [deployment-tools](), [developer-experience](), [devops](), [internal-tools](), [orchestration](), [performance](), [platform-engineering](), [sdlc](), [software-delivery](), [testing]() ### AI overview Datadog describes internal AI-enabled tools intended to improve developer experience, deployment support, and backend performance. One example is an agent-orchestrated support application that investigates deployment requests, drafts responses for approval, and can open a pull request when it identifies a potential fix. ### Source excerpt At Datadog, we don't just use AI to ship customer products. It also enables internal projects and performance wins that help our engineers and systems do their jobs better and faster. Learn more in this blog. ## 5 pitfalls to avoid when measuring DevEx in the AI era DevFeed: [5 pitfalls to avoid when measuring DevEx in the AI era]() Original publisher: [Read original article]() Author: Candace Shamieh; Teddy Gesbert Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [devex](), [code productivity](), [engineering-leadership](), [Artificial Intelligence](), [Code review]() Tags: [ai](), [ai-adoption](), [ai-impact](), [ci-cd](), [ci-visibility](), [collaboration](), [developer-experience](), [devex](), [devops](), [dora-metrics](), [engineering-management](), [internal-developer-portal](), [metrics](), [performance](), [productivity](), [review](), [software-delivery](), [test-optimization]() ### AI overview This article examines common pitfalls in measuring developer experience during the AI era. It argues that individual output metrics, including token consumption, lines of code, pull request counts, commits, and story points, can undermine trust and collaboration when treated as measures of personal productivity. ### Source excerpt Don't mistake AI adoption for productivity. Learn how to avoid 5 common pitfalls when measuring DevEx, with practices from Datadog engineering ## Introducing the Canary Federal Partner Program DevFeed: [Introducing the Canary Federal Partner Program]() Original publisher: [Read original article]() Author: Tom O'Reilly Brian Bridger Published: 2026-06-29T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Automation](), [Deployment](), [Security](), [cloud-infrastructure](), [Software]() Tags: [automation](), [cloud](), [compliance](), [deployment](), [engineering](), [government](), [security](), [software-delivery]() ### AI overview Harness announces the Canary Federal Partner Program, a co-selling initiative for federal-focused partners. The program combines Harness resources with partner expertise to support secure software delivery, automation, compliance, and government-focused go-to-market efforts. ### Source excerpt Discover the Harness Canary Partner Program for federal partners. Accelerate secure software delivery with AI, automation, compliance support, co-selling, and g | Blog ## How to migrate feature flags without breaking production DevFeed: [How to migrate feature flags without breaking production]() Original publisher: [Read original article]() Author: Anthony Rindone; Ryan Lucht; Natasha Silva Published: 2026-06-23T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [migration](), [SDKs]() Tags: [developer-tooling](), [devops](), [feature-flags](), [how-to](), [migration](), [production](), [safety](), [software-delivery](), [sync](), [validation]() ### AI overview A guide to safely migrating feature flags by auditing legacy flags, checking evaluation parity, synchronizing configuration, and validating cutover safety. ### Source excerpt Feature flag migrations have a reputation for stalling. Learn how to structure the process in a few steps: audit legacy flags, validate evaluation parity with shadow mode, and cut over with confidence. ## Harness Named a Leader in the 2026 Gartner® Magic Quadrant™ DevFeed: [Harness Named a Leader in the 2026 Gartner® Magic Quadrant™]() Original publisher: [Read original article]() Author: Harness Team Published: 2026-06-17T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [DevSecOps](), [AI Platform](), [AI-assisted coding](), [Artificial Intelligence](), [Software]() Tags: [2026](), [agents](), [ai](), [ai-agents](), [ai-coding](), [ai-platform](), [blog](), [devsecops](), [gartner](), [harness](), [platforms](), [software](), [software-delivery]() ### AI overview Harness announces that it was named a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third consecutive year and was positioned furthest on the report's Completeness of Vision axis. The article presents Harness's view that governed AI and autonomous agents will shape the future of software delivery. ### Source excerpt Harness has been named a Leader in the 2026 Gartner® Magic Quadrant™ for DevSecOps Platforms for the third consecutive year and positioned furthest in Completen | Blog ## Harness AI Verification and Rollback for Argo CD: Limitations of Static Thresholds DevFeed: [Harness AI Verification and Rollback for Argo CD: Limitations of Static Thresholds]() Original publisher: [Read original article]() Author: Prasad Satam Akshit Madan Shubhendu Patidar Published: 2026-06-17T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [argo-cd](), [GitOps](), [Kubernetes](), [Artificial Intelligence](), [Prometheus]() Tags: [ai](), [argo-cd](), [gitops](), [harness](), [kubernetes](), [machine-learning](), [maintenance](), [progressive-delivery](), [prometheus](), [rollback](), [software-delivery](), [statistical-significance]() ### AI overview This vendor commentary argues that Argo Rollouts Analysis Templates can be difficult to maintain because they rely on manually defined static thresholds. It presents Harness AI Verification and Rollback as a context-aware alternative that uses unsupervised machine learning and service health trajectories to assess deviations and support rollbacks. ### Source excerpt Stop the maintenance nightmare of static thresholds. Learn how Harness AI Verification and Rollback provides context-aware safety for Argo CD. | Blog [Next page]()