The Paradigm Shift

Nov 12, 2025

@NealWhittle brings us "The Paradigm Shift" - SDLC vs ADLC.

 

Neal states: "When hashtag#IBM announced a strategic partnership with Anthropic to accelerate the development of enterprise-ready hashtag#AI, it released a first-of-its-kind guide focusing on the Agent Development Lifecycle (ADLC). The report describes a paradigm shift from SDLC (Software Dev Life Cycle) to ADLC (Agent Dev Life Cycle). If you understand the three fundamental differences, you are well ahead of the crowd."

 

"From deterministic to probabilistic: software follows predictable execution paths, while agents make dynamic decisions."

 
"From static to adaptive: Software has fixed functionality while agents can learn and evolve."

 
"From code-first to evaluation-first: Traditional software metrics can't predict agent success, so the ADLC must rely on systematic measurement of agent behavior and evaluation of business outcomes." 

 

Here is a cheatsheet to help visualize "The Paradigm Shift" 

 

 

 

There is a corresponding IBM document titled: 

"Architecting Secure Enterprise AI Agents with MCP (Model Context Protocol) - A Strategic Guide to Agentic Development" which can be found here

 

@Gemini defines SDLC vs ADLC this way:

Software Development Life Cycle (SDLC)

• Definition: A process used in the software industry to design, develop, and test high-quality software.
• Focus: A broad, traditional approach to software development, often viewed as more linear.
• Phases: Typically includes phases like planning, design, development, testing, deployment, and maintenance.
• Goal: To produce high-quality software, minimize costs, and complete the project in the shortest possible time.   
  Agent Development Life Cycle (ADLC)
  • Definition: A new lifecycle specifically for developing AI agents, addressing their unique probabilistic and adaptive nature.
  • Focus: Building reliable, robust, and adaptive AI agents, with a strong emphasis on tuning and optimization.
  • Phases: Combines continuous loops for experimentation (between build and test) and runtime optimization (between operate and monitor) with traditional phases like design, build, and deploy.
  • Goal: To ensure successful, reliable, and secure AI agents, with continuous adaptation and governance integrated throughout the process.