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AI can write 1,100 lines of code for a loading spinner in five minutes. Your change board still meets once a week. Karthik Jayaraman, VP of Information Technology at Fiserv, explains what happens when code generation speeds up and everything downstream stays the same. He covers why "human in the loop" doesn't scale, why handing all review to AI backfires, and why an agent told not to touch production needs a boundary it can't cross, not just an instruction.

DORA's data shows that as AI adoption goes up, individual effectiveness rises, and so does software delivery instability: more rollbacks and more unplanned rework. Nathen Harvey of Google's DORA team explains why AI acts as an amplifier of whatever system you already have. He walks through the seven capabilities that separate teams getting real gains from teams drowning in downstream chaos. He also argues that the risks stopping you from shipping AI-built work should become your platform roadmap.

Everyone online seems to have a fully autonomous SDLC. Most enterprises are still at level 3, and they'll be there for years. In the EVOLVE 2026 opening keynote, Cortex co-founders Anish Dhar (CEO) and Ganesh Datta (CTO) lay out a practical maturity model for getting from AI coding agents to an AI software factory without blowing up cost, quality, or security along the way. They've watched hundreds of engineering orgs adopt AI over the last two years. This talk distills what separated the successful rollouts from the chaotic ones.

Small agent changes mean a small test scope. The agent knows exactly what it changed, so it can pull the 50 tests that matter out of a 10,000-test inventory and skip the rest. Arjun Iyer on why that's the validation model that scales.