Tag: cloudbees

  • Why Agentic Coding Needs Governance at Scale

    Why Agentic Coding Needs Governance at Scale

    Most enterprises never fully finished the DevOps transformation they started a decade ago. Agentic coding is now surfacing every place that was left unfinished. Builds still take hours. Test suites are flaky. Releases are still gated by manual steps. An AI coding agent that has to sit idle for eight hours waiting on feedback is not just slow — it is an extremely expensive kind of slow, burning tokens and calendar time on the same pipeline gaps DevOps was supposed to close years ago.

    Moritz Plassnig, the newly returned CEO of CloudBees, joins Alan Shimel to explain what he saw during five years away at Immuta and why he came back. Watching large language models reshape how customers worked with data reignited his interest in the delivery side of the problem, and he argues agentic coding is DevOps’s second chance to actually deliver on the original promise. The gap is no longer that teams disagree that DevOps is real — it is that most companies never went all the way, and the shortcuts they took are now the ceiling on how fast agents can work.

    Plassnig’s recipe is direct. Take the rules an organization already lives by — two-human review on any code change, whatever the specific policies are — and encode them as versioned policy that a central governance engine can enforce across CloudBees, Jenkins, GitHub, GitLab or whatever coding tool a team picks. He shares a striking data point from a very large bank: technology planning horizons have compressed from five to ten years down to two to three, which means standardization is losing to a curated menu of approved tools that a governance layer holds together.

    If code generation is no longer the constraint, the next bottleneck is getting software into production safely — and Plassnig is blunt that AI-scale development needs AI-scale governance or the mismatch becomes catastrophic. Supply chain risk compounds with every automated step, and the organizations that pretend policy can stay manual are the ones that will discover the hard way that agents scale faster than any human review process ever could.

  • Adoption and Fragmentation of AI in CI/CD—and Operational Readiness

    Adoption and Fragmentation of AI in CI/CD—and Operational Readiness

    In today’s enterprise software delivery environment, AI has moved from a distant possibility to an active area of experimentation across the CI/CD pipeline. The conversation around AI in software delivery often runs ahead of the underlying reality, and most organizations are still working through what it takes to operationalize AI inside delivery architectures that were built long before AI was a practical consideration.

    Techstrong polled its community of DevOps, platform engineering and software delivery professionals to understand where AI adoption in CI/CD really stands, where it is being applied today, and what is standing in the way of broader operationalization. Our findings reveal that active interest is running ahead of operational readiness, with most organizations either experimenting in isolated areas or beginning to expand AI use in selected parts of the pipeline, while fragmented toolchains, governance concerns and integration complexity remain the primary barriers to scale.

    Techstrong brings you this PulseMeter report sponsored by CloudBees, supported by our independent research data. Learn more by downloading the full report.

    CloudBees PulseMeter Report cover

    Download PulseMeter Report

  • MCP Emerges as a Catalyst for Modern DevOps Processes

    MCP Emerges as a Catalyst for Modern DevOps Processes

    Loreli Cadapan, VP of product at CloudBees, discusses how the Model Context Protocol (MCP) developed by Anthropic will provide the foundation for reinventing DevOps workflows.

    Cadapan explains that MCP acts as a bridge between large language models and enterprise DevOps environments, functioning as a kind of control plane. It can connect with different CI/CD tools and testing frameworks, pull in real-time data on pipelines and vulnerabilities, and even orchestrate tasks across the software delivery lifecycle. By reducing context switching and integrating with existing developer tools, MCP is designed to improve both productivity and governance.

    She compared MCP servers to factory floor managers—directing specialized AI agents that handle specific tasks such as diagnosing build failures or recommending tests. The challenge, she noted, is ensuring those agents are trained on the right data. As with any AI, the quality of output depends heavily on the quality of the inputs and feedback they receive.

    Cadapan acknowledged that AI adoption varies across organizations. Some want co-pilot style assistance with humans in the loop, while others are experimenting with allowing agents to fully automate parts of their workflows. Either way, she stressed that guardrails and policies must be in place so that outcomes are reliable, secure, and aligned with enterprise requirements.

    Looking ahead, Cadapan sees AI changing not just coding and pipelines but also product management practices, from drafting requirements to validating solutions. As AI-generated code and commits increase, bottlenecks are shifting downstream—from developers to reviewers and pipelines. Her advice for teams is to stay informed, experiment carefully, and validate outputs rather than trusting AI blindly.

    The broader message: MCP and AI agents are early steps toward rethinking the entire software delivery lifecycle. As software delivery accelerates and AI-generated code becomes commonplace, the real challenge will be ensuring pipelines, governance, and teams evolve at the same pace. For DevOps practitioners, that means preparing now for a future where AI is embedded in every stage of the lifecycle.