Autonomous AI systems are reshaping how we think about application logic. Where traditional chatbots execute predefined rules and hand off problems to users, agentic AI systems decompose complex goals into sequences of actions, make decisions independently, and iterate toward outcomes without human intervention. The distinction matters for builders. A chatbot might tell you how to fix a bug; an agentic system can inspect your codebase, write test cases, run them, refactor based on failures, and commit the result. This shift from instruction-following to goal-directed behavior creates new architectural challenges and opportunities, especially when agentic capabilities must integrate seamlessly into web applications where users expect transparent, controllable interfaces. Understanding Agentic AI vs. Traditional LLM Workflows The taxonomy of AI behavior in production systems centers on how much autonomy the system holds.…