No — but it helps tremendously. While most modern Agentic Agents use Large Language Models (LLMs) like GPT-4, Claude, or open-source models like Mistral and LLaMA, they are not the only option. Agentic behavior — memory, planning, tool use, goal pursuit — can also be modeled with rule-based systems, classical AI, and cognitive architectures.

🤖 Agentic Behavior Without LLMs

• Finite State Machines: Early chatbots and NPCs use conditional rules and state transitions to simulate goal-driven behavior (e.g., “If X, then do Y”).
• Symbolic Planners: Tools like STRIPS, PDDL, or GOAP (Goal-Oriented Action Planning) enable agents to formulate action sequences using logic and constraint solvers.
• Cognitive Architectures: Systems like ACT-R and SOAR implement memory, task switching, decision-making, and long-term goal planning — all without LLMs.
• Reinforcement Learning (RL): RL agents in robotics or games can learn agentic strategies through trial-and-error optimization (though these often lack language or flexible reasoning).

🧠 Why LLMs Have Become the Default

• Language Understanding: LLMs can interpret natural language prompts, questions, goals, and tool responses.
• Flexible Reasoning: LLMs can dynamically plan, revise, and summarize — unlike rigid logic trees.
• Few-Shot Behavior: Prompt engineering enables rich behavior without complex retraining or coding.
• Tool Integration: LLMs can interface with functions, APIs, and structured environments using JSON schemas or plugins.

🧩 Hybrid Approaches

• LLM + Planner: Use an LLM for high-level reasoning, but delegate task execution to symbolic or rule-based agents.
• LLM as Controller: Let the LLM decide which sub-agents or tools to activate based on current context.
• Fallback Systems: When the LLM fails (e.g., hallucination or confusion), hand off to a deterministic module for recovery.

💬 Example Use Case: Game NPC

A non-LLM agent can manage pathfinding, inventory, and scripted behavior. However, an LLM-powered overlay could give the NPC memory, social conversation, and dynamic quest logic — adding depth and believability.

📘 Summary

You don’t need an LLM to build an agentic system — but using one greatly simplifies the creation of flexible, language-capable, goal-driven agents. For many developers, LLMs serve as the cognitive engine, while traditional AI modules handle grounding, precision, and control.