Problem statement
Traditional automation has carried businesses far, but its rigidity is showing. Scripts, macros, and predefined workflows perform reliably only when every condition is expected. In today’s dynamic environments, that predictability rarely exists; inputs change, data shifts, and exceptions constantly appear. Large language models (LLMs) offer intelligence but lack persistence, memory, and structure to act as dependable systems on their own. The challenge is clear: we need a way to combine reasoning, adaptability, and tool use into workflows that don’t break under pressure. Agents address this gap, transforming raw generative models into goal-oriented, problem-solving systems.
