Modern LLM-based agents are specifically designed to avoid requiring every possible execution path to be predetermined. The uploaded course material therefore correctly identifies “Requiring every execution path to be predefined” as the behavior that is NOT characteristic of an agent.

OpenAI defines agents as systems capable of independently accomplishing workflows using an LLM to manage workflow execution and make decisions. An agent can determine when a workflow is complete, correct its actions after receiving observations, and dynamically select tools according to the current state. This differs fundamentally from conventional deterministic automation in which developers encode every branch and execution path beforehand.

Agents commonly pursue objectives across multiple reasoning-and-action cycles. They can invoke external APIs, databases, search systems, or other tools; inspect the resulting observations; and choose subsequent actions. A typical agent loop continues until an exit condition is reached rather than following one permanently fixed sequence.

Predetermined rules may still be used for safety, permissions, and guardrails, but the complete path toward the goal does not need to be pre-scripted.

Therefore, D is the correct answer.

Study Guide reference/topic: Introduction to AI Agents — autonomy, agent loops, observations, dynamic tool use, multi-step goal execution, and deterministic workflows.