As agentic AI moves from experimentation to production, engineering leaders need a disciplined framework for operationalizing it within the SDLC. Key principles include establishing business KPIs before deploying agents, measuring performance across the full development lifecycle, and embracing 'bounded autonomy' rather than unchecked agent behavior. Real enterprise deployments succeed through tight orchestration, staged rollouts, human-in-the-loop controls, and continuous instrumentation. The organizations that scale agentic AI effectively are those that prioritize governance, observability, and iterative improvement over raw autonomy.
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Agentic AI requires outcome-driven SDLCsEstablish business KPIs firstMeasure performance across the full SDLCWhat real enterprise deployments look likeWhat engineering leaders should focus on nowThe path to scalable agentic systemsSort: