The Agentic SDLC: The Software Lifecycle, Rebuilt Around Agents

The agentic Software Development Lifecycle (SDLC) involves AI agents taking over substantial parts of the software development process, including planning, coding, testing, reviewing, deploying, and operating, with engineers setting the intent and overseeing the results. This approach differs from traditional AI-assisted development by allowing agents to autonomously complete tasks across the lifecycle stages. Engineers transition from writing code to focusing on intent-setting, governance, and making critical decisions. While single-agent tasks are manageable, scaling across an organization can lead to "agentic chaos," emphasizing the need for a control plane that provides context, governed access, human checkpoints, and audit trails. This infrastructure is crucial to prevent chaos and maximize the efficiency and reliability of agentic SDLCs. The role of platform engineering is to create an environment where developers can efficiently build agentic workflows while maintaining standards, and software engineers must adapt by directing and reviewing agent outputs, focusing on design and architectural judgment. The overarching success of an agentic SDLC relies on comprehensive infrastructure that supports agent actions rather than the capabilities of individual AI agents themselves.