Agentic AI is breaking the traditional software development rules

The Agent Development Lifecycle – where AI agents are managed, not coded

Opinion By Gaurav Suman Published 14 October 2026

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AI Agents are unlike any software development witnessed before. Traditionally, trustworthy software systems have followed a structured approach to being built and developed. One that plans, builds, tests, and maintains software to ensure its reliability and scalability. This created the Software Development LifeCycle (SDLC) (1) framework.

But AI agents are different: they are a team of autonomous, probabilistic actors that work most effectively in a real-time, event-driven environment, enabling the agents to coordinate with real-time data to develop, learn, and work.

Gaurav Suman

Director of AI and industry solutions at Solace.

To scale an enterprise workforce of AI agents, organizations need to combine the principles of employee management with software development rigor. These agents are not static software that can be coded and developed, due to probabilistic language models. They are built, evaluated, and governed differently.

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This requires organizations to move from traditional SDLC to an Agent Development LifeCycle (ADLC) (2), which utilizes the structure and repeatable process of the SDLC framework and combines it with the development and management of AI agents.

A helping hand from HR

Since AI agents perform roles within an enterprise, the ADLC should draw on lessons from the human employee lifecycle.

When an organization brings on a new team member, there is a deliberate process: their role is defined; they’re given access to the systems they need; they are trained; and they are integrated into a team. Once they are empowered and put to work, a manager oversees their activity, monitoring and developing their performance over time.

As autonomous actors operating within a system of governance, AI agents deserve and require that same treatment.

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