The SDLC has compressed. Waterfall gave us months between intention and production, Agile gave us weeks. AI-assisted development gives us the cloudburst: intention becomes shipped code almost immediately. Most organisations are absorbing this flood with drainage systems designed for drizzle. This talk introduces a practical framework for AI adoption built on one governing rule: no compression capability unlocks until its matching reservoir exists. Compression (spec-driven development, agentic loops, AI-generated code at volume) creates velocity. Reservoirs (review architecture, policy-as-code, feature flags, chaos engineering) create the capacity to absorb it safely. Teams that build compression without reservoirs don't move faster; they flood. Drawing on experience shipping regulated digital identity products at Yoti, including production AI evaluation systems combining deterministic checks, LLM-as-judge, and human-in-the-loop validation, I'll close with the emerging accountability gap when AI agents act on behalf of humans, and why SREs will feel it first.
Alttaf is Director of Engineering & AI Innovation at Yoti, where he has spent nearly a decade shipping regulated digital identity products including eSignatures, verifiable credentials, and verified calls. He holds a patent in the eSignature space and has built production AI evaluation systems for regulatory monitoring. His work focuses on how engineering teams adopt AI safely: compressing delivery without outrunning their capacity to absorb it.
