Every engineering team is already generating code with AI. The harder problem is knowing how much of it should ship without a person "in the loop".  Most organizations are still deciding that case by case, with no real system behind it. In this keynote, we'll explore what that system could look like: an agentic framework that scales autonomy to the risk of each AI-generated change, not a blanket policy that treats them all the same. You'll see it in real demos, backed by customers already running it at production scale.

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.

As AI accelerates how fast code ships, security and governance can't stay bolted on at the end. This panel explores what it takes to build security and compliance directly into AI-speed delivery — from real-time threat detection and automated testing to governing the access and permissions of AI agents themselves. Panelists share how their organizations are closing the gap between security and engineering, embedding guardrails into the pipeline rather than the review queue, and staying compliant without slowing teams down.

AI coding assistants and autonomous agents are helping developers write code faster than ever. As the primary author of code shifts to be bots, what are the implications for DevSecOps pipelines and platforms? We'll discuss how leaders are adapting their approaches to meet the needs of teams using AI most aggressively.

AI has eliminated or reduced bottlenecks from code review backlogs to infrastructure provisioning delays to incident response times. But, as we saw in 2025, the bottleneck moved further down the pipeline. We'll look at present day data on how AI tools have shifted where teams spend their time, which traditional constraints remain relevant, what new bottlenecks have emerged in AI-augmented workflows, and how to unlock your AI investment potential.

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