This source from IBM Technology explains how to transition from a single prompt to an agentic workflow when solving complex problems. The presenter illustrates that even the largest language models often fail when tasked with multifaceted goals in a single "bite." By breaking a grocery data audit into distinct stages—extraction, classification, comparison, and generation—the speaker achieved a higher success rate than a single prompt allowed. This modular strategy involves using multiple LLM calls or specific functions to handle individual sub-tasks rather than relying on one general response. Ultimately, the text defines an agentic approach as a superior method for managing edge cases and intricate logical sequences that confuse standard AI interactions.
