Enable every agent to drive ROI with a robust agent development lifecycle
This post introduces the Agent Development Lifecycle (ADLC) as a structured approach to building and managing enterprise AI agents, mirroring the software development lifecycle. It details the stages of ADLC: Opportunity, Design, Performance, Context, Develop, Launch, and Monitor & Improve. New capabilities are introduced to support ADLC: generally available auto-mode agents for planning and acting without predefined workflows, debug and trace views for visibility into agent execution, sub-agents for modularity, agent sandbox for secure runtime environments with file systems and code interpreters, an agent library for discoverability and governance, agent access policies for organization-wide guardrails, and agent insights for monitoring usage and feedback. These features aim to enable repeatable, measurable outcomes and drive enterprise value from AI agents.
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