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.

Your teams are shipping more code than ever, and most engineering leaders are stitching together deploy frequency, MTTR, and vulnerability counts by hand to figure out if that output is actually landing as reliable, secure software — or piling up as invisible risk and tech debt. As agents write more of that code, the volume only grows, and the standard productivity metrics don't answer the question that matters: is this working? This 45-minute session gets you DRIVE-certified and shows the framework in use. DRIVE is Cortex's framework for measuring org effectiveness, built on five pillars: Delivery, Reliability, Initiatives, Vigilance, Efficiency. Taylor will also run a live Operational Excellence review using real data and the agenda a leadership team actually uses. You'll leave with: DRIVE certification and a self-assessment of your org A repeatable Operational Excellence review agenda A plan for the metrics to track your org's maturity

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?

Learn how Cortex's own software factory works and how you can build your own. This 45-minute session will begin with an overview of engrams, the in-house software factory built by Cortex, and how to deploy it within your organization. Learn from the experts by playing around in an open sandbox, building real AI SDLC automations. No credentials or tokens required. You'll leave with: Tips on how to effectively roll out your own software factory An understanding of how to automate your SDLC

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?