What is agentic SDLC? A complete guide for engineering leaders

Agentic SDLC describes a software development lifecycle in which AI agents autonomously plan, execute, and evaluate multi-step tasks across development, deployment, operations, and learning, while humans define goals and approve consequential or irreversible actions. Unlike autocomplete tools or fixed scripts, agents use contextual information from codebases, infrastructure, telemetry, deployment histories, and incidents to adapt their actions through feedback loops. Suggested applications include infrastructure management, code review, test generation and flaky-test triage, canary deployment monitoring, rollback recommendations, alert grouping, root-cause analysis, incident coordination, and automated post-mortem drafting. The article argues that the largest near-term benefits occur in operations, where agents can reduce the coordination and documentation burden of incident response, improve context available to on-call engineers, and potentially lower mean time to resolution. It emphasizes gradual adoption, integration with existing monitoring, paging, ticketing, and communication tools, and governance controls that preserve human approval for high-risk production changes, particularly during severe incidents.

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