At Notch, ADLC is the process behind every agentic AI system we build. Seven phases, Discovery through Integrate, with each producing a defined output before the next begins. Human oversight is mapped before production. Evaluation runs continuously after go-live. The methodology does not change with the project type, the team, or the scope.

SDLC governs deterministic systems. The same input produces the same output, every time. ADLC governs agentic systems, where behavior evolves based on context, prompts, models, and external tools. SDLC validates known code paths. ADLC evaluates reasoning quality and behavioral alignment. SDLC ends at deployment. ADLC treats deployment as the start of active monitoring, with evaluation running in production and feeding back into the build phase. The core difference is this: SDLC was built for software that executes. ADLC was built for software that thinks.

Agent Development Lifecycle is a structured methodology for building agentic AI systems. Where traditional software development assumes predictable execution, agentic AI systems reason, adapt, and act across tools and environments. ADLC organizes that complexity into defined phases, each with a clear output. Discovery, Design, Build, Agentic Experience, Human-in-the-Loop, Evaluate, and Integrate. Human oversight is designed in from the start. Evaluation covers reasoning quality, not just output accuracy.

The Agent Development Lifecycle (ADLC) is a structured methodology used by Notch for designing, building, evaluating, and deploying agentic AI systems. Agentic systems reason, adapt, and act across tools and environments. Small context changes compound into materially different outcomes. ADLC is built for that reality. Seven phases, each with a defined output. Human oversight designed in from the start. Evaluation that covers how the agent reasons, not just what it produces.