AI agents powered by large language models (LLMs) require enhanced application development lifecycles that address the unique nature of agent development. Unlike static applications, agents are adaptive, interactive systems that must be continuously evaluated, secured, governed, and improved due to the nondeterministic and probabilistic nature of underlying LLMs. For example, a traditional software development cycle (SDLC) would deploy an agent to production after successful staging tests. However, the same agent tested with identical data can produce different results due to inherent variability. 

This behavior necessitates different testing and validation approaches—a key differentiator from the application development lifecycle.

This guide presents the agent development lifecycle (ADLC), a structured approach to designing, deploying, and managing enterprise AI agents. At its core, it is an operational discipline based on standard DevSecOps practices that ensures agents remain safe, reliable, secure, and aligned with organizational and regulatory goals (such as compliance with AI Regulations).