Tag: software delivery

  • The Next DevOps Frontier: Governing Models, Agents and Code

    The Next DevOps Frontier: Governing Models, Agents and Code

    Anaconda CEO David DeSanto explores what it takes to make AI-native development practical, secure and scalable for modern engineering teams. DeSanto reflects on his journey from leading product at GitLab to stepping into the CEO role at Anaconda, and why the next wave of software delivery will be shaped by the intersection of open source, DevOps practices and AI governance.

    Software is increasingly assembled rather than written. With dependencies pulled from countless repositories, the software supply chain has become one of the largest sources of risk—especially as attackers target packages and organizations push more workloads into production faster. DeSanto explains how trust, curation and reproducibility matter when teams are depending on thousands of third-party components to build and ship applications.

    From there, they turn to the evolving needs of AI development workflows. Beyond packages, teams now have to account for models, agents and new “AI supply chain” artifacts that also require security controls, policy enforcement and governance. DeSanto outlines how organizations are trying to move from experimentation to production with guardrails in place—tracking lineage, performance characteristics and vulnerabilities, and creating an “AI bill of materials” that extends the same discipline DevOps teams expect in traditional pipelines.

    A standout theme is developer enablement. As AI raises the cost and complexity of building and running models, the ability to test and iterate locally—then carry those changes forward into production—becomes a practical advantage. DeSanto emphasizes a hands-on approach: staying close to builders, listening to customers, and making it easier for teams to adopt new capabilities without sacrificing control.

    As AI becomes part of everyday software delivery, the winning platforms will be the ones that reduce friction for developers while strengthening security and governance across the full lifecycle.

  • Empowering Secure, Agentic Software Delivery

    Empowering Secure, Agentic Software Delivery

    At swampUP 2025 in Napa Valley, JFrog co-founder and CTO Yoav Landman joined Alan to reflect on the company’s latest announcements and the broader direction of software delivery in the AI era. Landman noted that this year’s event may have been JFrog’s most ambitious yet, with a wave of product releases designed to tie together themes of trust, automation, and AI-driven DevOps.

    One highlight is the company’s push to unify governance through AppTrust, which provides end-to-end traceability and compliance across the software supply chain. The aim is to ensure that every artifact — whether human- or AI-generated — can be validated and trusted before moving through the pipeline.

    Another centerpiece is JFrog Fly, billed as an “agentic repository” that enables AI agents to securely interact with APIs and development workflows via the Model Context Protocol (MCP). As AI coding tools become commonplace, Fly is intended to help DevOps teams manage the complexity of agent-driven development while maintaining control and visibility.

    Landman emphasized that these aren’t abstract roadmaps — they’re production-ready features shaped directly by customer feedback. The feedback loop from swampUP, he noted, often influences products within months rather than years, especially as AI accelerates iteration cycles.

    For Landman, the common thread across these initiatives is addressing the realities of modern DevOps: accelerating release velocity while ensuring governance and security don’t lag behind. With software supply chains under constant pressure and AI poised to reshape workflows, he argued that platforms like JFrog must become the system of record for DevOps in order to keep pace.

    The conversation underscored the urgency: in a world where AI is changing how code is created and deployed, trust and automation aren’t optional — they’re foundational.

  • Feature Management’s Role in DevOps with Trevor Stuart

    Feature Management’s Role in DevOps with Trevor Stuart

    Trevor Stuart, general manager and senior vice president of Harness, explains how feature management is becoming a critical extension of DevOps workflows a year after the acquisition of Split Software. 

    Feature management is fast becoming a critical pillar in modern DevOps workflows, especially as organizations seek to keep up with the rising volume and velocity of software development. Stuart discusses how the evolution of feature flags has reshaped deployment practices and the expectations of software delivery teams.

    The core value of feature flags lies in their ability to enable safer, faster releases. They give teams the flexibility to toggle functionality without redeploying code, making it possible to mitigate risk during production rollouts. Initially used sparingly and often managed via manual config files, feature flags are now a mainstream method for progressive delivery. They help organizations validate features with specific user segments, reduce blast radius when issues arise, and support experimentation in real-world conditions.

    Stuart highlights that the growing use of AI-generated code, modular microservices, and increasing product personalization have only amplified the need for robust feature flag systems. These tools now serve not just as toggles, but as real-time control layers for dynamic configuration, adaptive experimentation, and telemetry-based feedback. Companies are increasingly using them to fine-tune AI models, personalize customer experiences, and refine software quality using real-time performance data.

    Despite broad awareness, many teams still rely on homegrown or brittle solutions, leading to maintenance issues and outages. The challenge is often organizational: determining whether to build, buy, or integrate these capabilities into broader CI/CD pipelines. Stuart argues that deeply integrating feature flagging into delivery workflows—particularly as part of a unified platform—is essential for scale and consistency.

    As software complexity grows, so does the need for intelligent automation. Stuart envisions pipelines that not only deploy features but also observe impact, run experiments, adapt based on results, and even feed signals back into models. It’s a shift from manual gates to data-driven automation, marking a pivotal transition in how DevOps teams manage risk, velocity, and innovation.