Rules-based workflows execute deterministic IF/THEN logic on structured data and always produce the same output from the same input. AI agents use LLMs to interpret unstructured input and dynamically choose tools, trading reproducibility for flexibility on messy tasks. Agentic workflows embed AI into predefined code paths where LLMs and tools are orchestrated through fixed processes for predictable outcomes. Invoice parsers built only on rules break when layouts change, while agents without scoped permissions and human approval lose audit trails. Governance favors fixed workflows because a set path is easier to log and reproduce than an agent chain that retries and converts errors into model messages.