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DevOps in the Age of AI Native
DevOps is entering its most transformative era since its inception. As organizations adopt AI native architectures, integrate machine intelligence into every stage of the software lifecycle, and push toward unprecedented delivery velocity, the very definition of DevOps is evolving. Automation is no longer just about pipelines — it’s about intelligent systems that learn, adapt, and optimize themselves.
“DevOps in the Age of AI Native” brings together engineering leaders, platform architects, and innovators to explore how AI is reshaping DevOps practices, tooling, and team dynamics. This session examines the shift from manual workflows to autonomous delivery, the rise of AI augmented engineering, and the new expectations for reliability, governance, and developer experience in an AI native world.
Attendees will gain a clear understanding of how DevOps is maturing in 2026 — and what it takes to build a delivery organization that can thrive in an era defined by intelligence, automation, and continuous adaptation.
Speakers
- Anthony Anter DevOps Architect & Evangelist, BMC Software Inc.
- Russell Fishman Senior Director of Global Solutions and Field Activation - NetApp
- Thomas Froment Program Lead, Development Tools - Eclipse Foundation
- Noam Levy Field CTO - groundcover
- Mike Vizard Chief Content Officer, Techstrong Group
event summary
How AI-native development is forcing DevOps to evolve
In this discussion, leaders from BMC, NetApp, Eclipse Foundation, and groundcover explore how DevOps practices must adapt for an AI-native world. Topics include the growing role of observability, evolving approaches to testing and governance, the rise of agentic engineering, and the changing responsibilities of software engineers as AI becomes embedded throughout the development lifecycle.
core insights
1
AI is a force multiplier
AI can dramatically increase development speed and efficiency, but it can also amplify poor engineering practices, security risks, and operational issues. Organizations must ensure that automation is supported by sound engineering, testing, and governance practices.
2
Traditional testing alone is no longer enough
As AI-generated applications become more dynamic and complex, production environments increasingly serve as an extension of the testing process. Success depends on rapid feedback loops, automated validation, and the ability to identify and respond to issues quickly.
3
Observability becomes a strategic capability
The panel emphasizes that organizations need richer signals, faster feedback, and greater visibility into application behavior to manage AI-driven software delivery effectively. Observability is becoming a foundational capability for operating AI-native systems at scale.
4
Agentic engineering is changing software delivery
AI agents are becoming active participants in software development and operations. Organizations must establish guardrails, governance policies, and security controls that allow agents to accelerate work while minimizing risk.
5
Software engineers are becoming orchestrators
Rather than focusing exclusively on writing code, future engineering teams will increasingly supervise AI-driven workflows, connect systems, manage autonomous agents, and ensure organizational knowledge and governance are incorporated into decision-making processes.
What You'll Learn
- How AI-generated code is reshaping software delivery and DevOps practices.
- Why observability and rapid feedback loops are becoming essential in AI-native environments.
- Practical approaches to balancing speed, governance, security, and resilience.
- What agentic engineering means and how AI agents are changing development workflows.
- How software engineering roles are evolving as AI becomes part of the development lifecycle.
- Best practices for reducing risk while accelerating innovation with AI-native technologies.
- How leading organizations are preparing for autonomous, self-healing, and AI-assisted software systems.
Key takeaways:
- Embed operations, security, governance, and resilience considerations from the start of development initiatives.
- Build automated testing and validation capabilities that can keep pace with AI-accelerated delivery.
- Invest in observability platforms that provide rapid insight into production behavior and emerging issues.
- Establish clear guardrails, permissions, and auditing mechanisms for AI agents.
- Design architectures and delivery processes that can adapt as AI models, tooling, and regulatory requirements continue to evolve.
- Focus on reducing feedback-loop latency rather than attempting to eliminate all risk before deployment.
“AI is a force multiplier. It's going to be a force multiplier for efficiency. It could be a force multiplier for quality depending on how you do it, but it's also going to be a force multiplier for bad behavior.”
Anthony Anter, BMC Software