Agentic SDLC vs STLC: What Changes in Each Life Cycle

Agentic software development life cycles use AI agents to plan, implement, test, deploy, and maintain software under human supervision, while an agentic software testing life cycle adapts traditional testing phases to independently establish whether agent-produced changes are correct. The central concern is that code and tests created by the same agent may share the same misunderstanding of a requirement, making passing tests insufficient evidence of correctness. The text argues that effective agentic testing requires separate planner, executor, and reviewer roles; explicit acceptance criteria captured outside the implementation prompt; reproducible environments; machine-readable, reconstructable, requirement-linked entry and exit criteria; and evidence that can be traced from requirements to tests, runs, and defects. It emphasizes that AI is most mature in phases with executable, objectively verifiable feedback, while judgment-heavy earlier phases remain less reliable. The discussion contrasts open-ended coding agents such as Devin with bounded verification tools such as TestMu AI’s Kane CLI, presenting independent, capped, machine-readable checks and evidence packs as more suitable for pull-request gates. Teams are advised to begin by independently testing a high-risk production flow, automate verdict handling, and build traceability before scaling the volume of agent-generated changes.

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