Agenda  

AI has crossed the threshold from helping developers write code to helping teams build entire systems. The next frontier is how enterprises architect the SDLC so they can trust what gets shipped. As agents move from autocomplete to planning, delegation, implementation, testing, and review, software leaders face a new challenge: preserving developer flow and speed while strengthening accountability, security, auditability, cost discipline, and control.

In this keynote conversation, Jyoti Bansal and Jeff Wang explore the new operating model for AI-era engineering, from the agentic developer workspace where humans and AI collaborate, to the enterprise delivery layer that verifies what is safe to release. They’ll discuss what humans should still own, where agents can safely act, what evidence should be required before, during, and after release, and how delivery data can make the next generation of AI development smarter. In the AI-native enterprise, trust is not assumed — it is continuously verified.

As agentic AI reshapes how software gets built and shipped, platform and developer experience teams are at the center of the transformation. In this 1:1 conversation, Cindy Wang shares how Workday is evolving its CI/CD infrastructure and developer experience at enterprise scale to keep pace with AI-driven delivery — from modernizing platform architecture to maintaining developer velocity, operational efficiency, and reliability as agentic capabilities become embedded in every stage of the SDLC.

Every enterprise wants to move faster with AI, but somebody still has to catch what AI gets wrong. Neil Watkins runs security, compliance, and global privacy for Burns & McDonnell, and he doesn't see security as a gate; he sees it as the windshield in a storm of AI bugs. We'll talk about how Harness secures, governs, and standardizes software delivery at the pipeline gate, and how that removes security as a blocker to AI adoption.

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Security practices have evolved to match the pace of AI-assisted development and automated deployments. Learn about real-time threat detection, automated security testing that keeps up with rapid release cycles, and how enterprise security teams are leveraging AI to identify vulnerabilities faster than developers can introduce them.

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Every platform engineering team is facing the same tension right now: developers want to move faster with AI, and the platform has to keep up without compromising the governance, compliance, and reliability that enterprise organizations depend on. In this session, we explore what it looks like to navigate that tension in highly regulated environments — from rethinking golden paths and toolchains to maintaining developer trust as AI coding agents become embedded in every stage of the SDLC. What does it actually take to build a platform that enables AI-driven velocity at enterprise scale without leaving governance behind?

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AI coding tools are everywhere. But most engineering teams are seeing incremental gains when transformational results are possible. In this conversation, Martin Harrysson, Senior Partner at McKinsey, draws on the latest research to unpack why the gap exists and what the top-performing engineering organizations are doing differently. David Pan, Field CTO at Cursor, joins to share what he sees on the front lines of AI-assisted development: how the best teams are moving beyond autocomplete to fundamentally rethinking how software gets built, reviewed, and shipped. Together they explore what engineering excellence actually looks like in the AI coding era, and what it takes to get there.

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The biggest bottleneck has become more nuanced than velocity: it's trust. As AI agents take on more of the build-deploy-secure cycle, the teams winning aren't just moving faster. They're moving with confidence. We break down what context-native AI actually looks like in practice, and how high-performing engineering orgs have built the trust frameworks that let them ship without second-guessing every step.

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