What is the AI-native SDLC? AI SDLC, AI-DLC, and ADLC explained

An AI-native software development lifecycle redesigns planning, coding, testing, review, release, and maintenance around AI participation, often including autonomous agents that perform multi-step tasks, whereas an AI-assisted SDLC mainly adds suggestion tools to otherwise unchanged human-led workflows. The distinction centers on whether AI merely improves individual tasks or reshapes handoffs, controls, review practices, and accountability across the lifecycle, with many organizations currently operating somewhere between these models. Terminology remains inconsistent: AI SDLC can refer either to developing software with AI or to building AI systems, while AI-DLC commonly denotes AWS’s specific AI-Driven Development Life Cycle methodology rather than a generic AI-driven process. ADLC also has two meanings, referring either to Endor Labs’ Agentic Development Lifecycle, in which coding agents work autonomously within conventional software development, or to the lifecycle for building, deploying, and monitoring AI agents themselves. From an application security perspective, agentic development requires teams to secure both the software agents produce and the agents’ capabilities to access code, install dependencies, run commands, and open pull requests.

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