Agentic AI Prompts a Shift From SDLC to ADLC

Enterprise teams may need to rethink how they build and manage software as they adopt autonomous AI agents, according to a TechRadar article by Gaurav Suman, director of AI and industry solutions at Solace.

Traditional software development follows the Software Development Life Cycle (SDLC), a structured process for planning, building, testing and maintaining systems. Suman argues that AI agents behave differently: they are probabilistic, autonomous actors that work with real-time data and coordinate in event-driven environments.

The article proposes an Agent Development Life Cycle (ADLC), combining repeatable software development practices with methods for managing agents through their organizational life cycle. That includes defining each agent’s role and boundaries, provisioning access to relevant systems, evaluating its performance, and supervising its actions.

Suman also argues that enterprise agents should be specialized and coordinated through an agent mesh, rather than built as monolithic systems expected to handle every task. In this model, agents communicate asynchronously, while role-based access controls limit each agent to the data and systems it needs. An agent mesh could also support testing during development and route selected actions to human reviewers before execution.

The article presents ADLC and agent mesh as approaches for addressing reliability, coordination and governance as organizations deploy AI agents. These are the author’s recommendations, not reported results from a named implementation.