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.
