Agent development lifecycle emerges as new software engineering discipline
Traditional software delivery practices are insufficient for agentic AI systems because agents behave non-deterministically and adapt based on context. A new agent development life cycle (ADLC) extends conventional practices with continuous evaluation, observability, identity, tool access, and governance. Teams must define agent scope and success criteria before coding, then design for dynamic decision-making.
The article explains that agents differ from traditional software because they make their own decisions about how to complete tasks, influenced by models, prompts, tools, and context. The ADLC adds continuous evaluation, observability, identity, tool access, and governance to every stage. Teams must first define the agent's scope and success metrics, such as handling 95% of valid requests without human help.
Design decisions include choosing between single or supervised multi-agent patterns, restricting tool permissions, and setting evaluation criteria for quality, safety, latency, and cost. The process emphasizes that evaluation continues throughout the agent's life cycle, not just at the final output stage.
This shift could affect how organizations deploy AI in customer service, healthcare, and finance, where non-deterministic behavior introduces new risks. Teams may need specialized skills in agent evaluation and governance, potentially reshaping job roles. Society could see more autonomous systems handling sensitive tasks, raising questions about accountability when agents make unexpected decisions. The emphasis on explicit approval and boundaries may help build trust, but the complexity could slow adoption for smaller organizations.