Building an AI-Native SDLC for Enterprise Adoption
Alex Lieberman and CJ Hess discussed on the Latent Space podcast how Tenex builds an AI-native software development lifecycle (SDLC) for the Fortune 2000. The episode explores paying for outcomes, building with agents, and overcoming the last mile of enterprise AI adoption. This discussion provides crucial insights into how large enterprises can transition from experimental AI tools to fully integrated, AI-native development workflows. It addresses real-world governance, automation, and accountability challenges faced by major organizations looking to scale software engineering. The 44-minute episode focuses on practical strategies for shifting software delivery models where AI handles labor, deterministic code enforces guardrails, and humans manage exceptions. It highlights how Tenex utilizes multi-agent systems to tackle complex enterprise requirements.
## BACKGROUND
The software development lifecycle (SDLC) traditionally follows a structured, linear process of planning, building, testing, and deployment. An AI-native SDLC reimagines this framework by embedding AI agents as collaborative partners throughout the entire workflow, transforming intent directly into executable delivery.