The Dirty-dozen of Agentic AI traps
This list started as an idea for a short LinkedIn post, but ended as a summary of systemic problems that need to be addressed to make coding LLMs/AI Agents a paradigm shift in software engineering—not a dead end that creates as many problems as it solves. Perhaps this attempt to organize my own thoughts on the topic will be of some use to anyone. Misunderstanding of intelligence: LLM-based agents do not reason in the human sense of the word; but are very advanced prediction and pattern recognition engines—which defines their unavoidable limitations inherent to the transformer architecture. Contrary to the marketing, they are not “intelligent” - defined here as the ability to generalize, abstract, and establish causal relationships between facts. They just simulate this process very convincingly using language as a medium - being a “Stochastic parrot”1, constrained by both training and syntax to the benefit of prediction accuracy - especially in the context of programming. ...