CI

Evidence (20)

• 7The New Stack·9/30/2026product_launch
  CloudBees shifts to an AI-first strategy

  CloudBees CEO Mo Plassnig says the company is pivoting to an AI-first model as generative AI rapidly increases software output in enterprise environments. He argues that enterprise DevOps teams will need stronger process, governance, and security controls to manage the growing volume of machine-generated code.

• 7The New Stack·9/30/2026regulation
  CRA readiness begins in the codebase

  The article argues that compliance with the EU Cyber Resilience Act is driven less by policy statements than by engineering controls embedded in the software lifecycle. It highlights the need for secure defaults, traceable releases, and evidence-backed change management to report vulnerabilities and incidents credibly.

• 5InfoQ·9/30/2026framework_update
  Treating Context as Code for AI Agents

  Patrick Debois argues that engineering teams should manage context for AI coding agents with the same discipline used for software code. He describes applying practices such as testing, CI/CD, package management, and security scanning to context so organizations can scale agents, control non-deterministic outputs, and preserve institutional knowledge.

• 5The New Stack·9/24/2026product_launch
  Kubernetes self-service splits responsibilities between developers and platform teams

  The article examines the tension between developers, who want fast access to Kubernetes environments, and platform teams, who remain responsible for cost, access control, policy, and lifecycle management. It argues that the practical approach is governed self-service through approved catalogs and integrated platforms rather than direct access to raw Kubernetes APIs.

• 4GitHub Blog·9/23/2026research
  GitHub survey finds developers want better tools for greener software

  A GitHub and Yale survey of 1,039 GitHub users found strong interest in tools, best practices, and measurement methods for making software more energy-efficient. Respondents also showed high concern about climate change and AI’s environmental impact, but the report notes the sample is not representative of all developers.

• 7The New Stack·9/22/2026security
  AI coding agents need secret-safe context boundaries

  The article argues that AI coding agents can unintentionally expose secrets because they ingest broad local context such as source files, logs, and environment data. It says traditional security checkpoints like code review and CI are no longer enough, and organizations need controls that prevent secrets from crossing from local development environments into model context or external provider systems.

• 6Hacker News·9/21/2026framework_update
  Linear speeds up CI to keep pace with AI coding

  Linear says AI coding increased pressure on its CI pipeline, so it reworked infrastructure and test execution to reduce wait times and runner usage. The company reports that despite a much larger test suite, pull request wait time fell from over 6 minutes to just above 5 minutes, while runner time per test roughly halved.

• 5InfoQ·9/7/2026research
  From AI Agent Demo to Production: Automated Testing

  Zhou Yu explains why AI agents often fail to move beyond demo environments and argues that simulation-driven testing can address compliance and reliability issues. The presentation highlights how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents and detect edge cases before deployment.

• 4InfoQ·8/25/2026framework_update
  Continuous Delivery Practices for Foundational Platforms

  Ian Nowland explains why standard CI/CD approaches often fail for stateful core infrastructure and foundational platforms. He outlines practical methods such as progressive deployments, synthetic testing in production, and blast-radius reduction to improve release safety in complex systems.

• 5The New Stack·8/22/2026framework_update
  Open Mainframe Project pushes mainframes into modern digital workflows

  The article argues that the Open Mainframe Project and Zowe are helping reposition mainframes as central components of modern enterprise architecture rather than isolated legacy systems. By exposing mainframe capabilities through APIs and integrating them into DevOps and cloud-native workflows, organizations can extend reliability and governance into end-to-end digital operations.

• 6The New Stack·8/20/2026product_launch
  Warp launches Factories for building cloud software factories

  Warp introduced Warp Factories, an open infrastructure layer for building cloud software factories that automate work across the software development lifecycle. The company says the platform is designed to improve coding-agent ROI and governance through built-in metrics, evals, self-improvement loops, and centralized control.

• 6The New Stack·8/7/2026regulation
  The AI kill switch problem is knowing what to shut down

  The article argues that public talk of an "AI kill switch" simplifies a much harder operational problem: identifying exactly which systems, services, and dependencies must be stopped when an AI capability poses unacceptable risk. It notes that as AI moves into production environments, shutdown authority is only useful if organizations can trace and constrain complex, interconnected infrastructure quickly and reliably.

• 6InfoQ·8/7/2026product_launch
  Spotify Details AI Agent for Fleet-Wide Codebase Migrations

  Spotify engineers Jo Kelly-Fenton and Aleksandar Mitic describe Honk, an AI coding agent built to manage large-scale codebase migrations across thousands of repositories. The talk focuses on separating CI verification runtime from the agent, handling pull request bottlenecks, and enforcing standardization across the engineering fleet.

• 7The New Stack·8/6/2026framework_update
  Anthropic pushes per-agent git worktrees, exposing infra bottlenecks

  Anthropic’s guidance to use one git worktree per coding agent makes parallel agent workflows the default, but it also exposes limits in CI, staging, databases, and other shared infrastructure. The article argues that branching now needs to extend beyond code into the runtime stack so each change can be tested end to end.

• 4Hacker News·8/3/2026research
  AI’s Limited Productivity Gains for Developers

  The article argues that AI has improved engineering productivity, but less than many leaders expect because most developer time is spent on planning, debugging, reviews, and coordination rather than writing code. It says junior developers may benefit more than senior engineers, since AI helps most with coding tasks and less with broader software design and decision-making work.

• 5The New Stack·7/31/2026framework_update
  Thoughtworks Engineer Says AI Software Needs Human Oversight On the Loop

  Thoughtworks distinguished engineer Kief Morris argues that teams using AI to build software need humans “on the loop” to define what counts as good code and good systems. He says production-ready CI/CD, tests, and guardrails must be built into agentic workflows now to keep software safe, secure, and maintainable. The article frames continuous delivery practices as the way to control AI agents rather than relying on post-hoc fixes.

• 6The New Stack·7/29/2026research
  AI strategy shifts from coding speed to delivery throughput

  The article argues that many companies invest in AI to make developers code faster, but coding is only a small part of software delivery. It says the biggest gains come from applying AI across the full software development lifecycle, especially in workflows like testing, reviews, incident response, and DevSecOps. The piece cites large regulated enterprises as examples of where AI can deliver measurable value when paired with mature CI/CD and automation.

• 7The New Stack·7/29/2026research
  Shipping Code Without Human Verification

  The article argues that agentic coding is making traditional human code review a bottleneck, especially for verification tasks. It proposes shifting teams toward automated verification systems and a “human on the loop” model, where people maintain the review infrastructure instead of checking every change themselves.

• 5The New Stack·7/25/2026framework_update
  Build AI Context Systems Instead of Fixing Code

  The article argues that software teams should stop trying to correct AI-generated code line by line and instead improve the surrounding system, context, and workflows. It frames this as a broader shift from deterministic software development toward context engineering, where organizations package and scale the information agents need to work effectively.

• 6The New Stack·7/24/2026research
  Test data delays are becoming the main bottleneck for AI-era delivery

  The article argues that AI has accelerated code and test generation, but many organizations still wait too long for production test data, creating a new delivery bottleneck at the validation stage. It cites survey findings that most enterprises rely on manual provisioning steps and need better automation, masking, policy enforcement, and self-service access to compliant test data.