Agenda

Most engineering organizations have adopted coding agents, but few have a framework for what comes next. In this keynote, Cortex founders Anish & Ganesh introduce a maturity model for the AI software factory: a multi-level progression that runs from teams using coding agents in isolation to a fully self-improving, autonomous SDLC that introspects, identifies its own weaknesses, and improves over time. They'll discuss where most companies sit today and what separates organizations stuck at Level 1 from those moving up the curve. Throughout, they'll share how Cortex operationalizes each stage, with evidence from Cortex's own engineering team: the practices run, the gates automated, and a first look at the AI software factory Cortex runs on itself.

AI's productivity surge masks a complex reality: it shifts developer friction from writing boilerplate to a new "verification tax" of auditing machine-generated code. DORA research reveals AI is an amplifier. It magnifies the strengths of high-performing teams, but in chaotic environments, it accelerates technical debt and cognitive load. We will explore the AI adoption "J-Curve" to understand why early dips in flow and stability are expected. Using the DORA AI Capabilities Model, we'll show how quality internal platforms and healthy data ecosystems empower developers to focus on meaningful work. Move beyond the hype of raw code generation. Learn to clear systemic roadblocks and build an environment where AI truly amplifies developer experience, creativity, and flow.

Find out whether your AI output is creating value or just accumulating risk. Led by Taylor Schmidt, Director of Customer Education and Delivery, Cortex. Your teams are shipping more code than ever, and most of the frameworks meant to measure the success of an engineering organization look at developer productivity. They fail to measure whether accelerated output is landing as reliable, secure software or piling up as invisible risk and tech debt. DRIVE measures organizational effectiveness in the age of AI, across five pillars covering Delivery, Reliability, Initiatives, Vigilance, and Efficiency. This certification workshop takes you through each pillar, how the signals fit together, and how DRIVE complements the DORA metrics you likely already track rather than replacing them. By the end you'll be able to: Assess where your organization stands on each of the five DRIVE pillars Explain each pillar and its signals to the engineers and executives you report to and with Walk out DRIVE-certified, with a plan for the metrics you need to track your org's maturity going forward No prior DRIVE or Cortex experience needed. Come with a rough picture of how your org ships today and you'll leave with a structured way to measure it.

Migrations and modernization programs are often slow, political, and difficult to finish. AI-accelerated throughput raises the stakes: more services, more dependencies, and more code shipping before anyone agrees on what "ready for production" means. This panel examines how leaders turn production readiness into a standard instead of a launch-day scramble, keep large migrations on track across dozens of teams, and modernize legacy systems without freezing delivery. Panelists share what worked, what stalled, and what they would do differently. Sample questions: How do you define "production-ready" so it holds as a standard, not a one-time checklist? What makes a migration actually finish instead of stalling at 80%? How do you keep legacy modernization moving without freezing feature work? Where does AI-generated code create readiness gaps you didn't have before?

At the leadership level, the problem is rarely too little data. It's parsing signal from noise. It's parsing signal from noise: deploy frequency, MTTR vulnerability counts scattered across teams. By the time someone's stitched it together by hand, you're staring at a wall of numbers, unsure what's changed or where to focus. As agents push more code, the volume grows. This workshop walks you through the Operational Excellence review, using DRIVE, a framework built around five pillars: Delivery, Reliability, Initiatives, Vigilance, and Efficiency. You'll work with real data in Cortex and learn how to run the review itself, end to end. You'll leave with: A repeatable OpEx review agenda for your leadership team Which DRIVE signals belong in that room, and which to cut A way to turn the review into clear & owned action items Bring a laptop. You'll work directly in Cortex.

As agents absorb more of the execution across the SDLC, every service that ships faster becomes another unit to own and secure, and aggregate risk compounds well ahead of any single team's ability to see it. Operational Excellence is how engineering leaders keep the system accountable: measuring where the org actually stands, catching risk before it becomes an incident, and moving people and attention to where they matter most. This panel brings together leaders who run recurring operational reviews and treat their organization as something they can observe and improve, not just react to. Sample questions: What does an operational excellence review look like in practice, how often should you run one, and who should be in the room? Which signals tell you output is outpacing your ability to operate it safely? How do you decide where to spend engineering time when everything looks urgent? What breaks first when velocity jumps 10x, and how do you catch it early?