While often related, LLM Agents and Agentic Agents are distinct in scope and capability. LLM Agents focus on executing tasks using a language model and tools, while Agentic Agents are designed with persistent identity, long-term goals, memory, and autonomy — mimicking human-like agency.

📊 Side-by-Side Comparison

Feature	LLM Agent	Agentic Agent
Core Focus	Task execution using LLM + tools	Simulated autonomy, memory, and goal evolution
Session Behavior	Often stateless or short-session agents	Long-lived, memory-aware, persistent identities
Memory Usage	Optional and task-specific	Essential for personality, learning, social context
Goal Formation	Prompt-driven or reactive	Self-directed, multi-day or evolving goals
Tool Use	Efficient, structured, bounded	Integrated into broader, goal-seeking behavior
Identity & Persona	Optional; often generic or single-role	Persistent; may simulate personality or memory-based selfhood
Example Use Case	Answer a user’s query using search + summarization	Remember user’s background and follow up months later with suggestions

🧠 LLM Agent Characteristics

• Designed for discrete tasks, often stateless
• Usually triggered by user input or a job queue
• Powerful reasoning + tool use in a single session
• Memory and planning are optional or external

🧠 Agentic Agent Characteristics

• Structured around long-term autonomy
• Often includes persistent memory, emotion, social context
• Inspired by human-like cognitive architectures
• Uses LLMs, but adds memory, reflection, identity, and goal evolution layers

📘 Real-World Analogy

• LLM Agent: Like a helpful freelancer who completes tasks on demand
• Agentic Agent: Like a colleague who remembers your history, adapts to your goals, and manages ongoing work independently

🤖 Do They Overlap?

Yes — many agentic systems are built on top of LLM agents. You can think of LLM agents as the foundation (tool use + planning), while agentic agents layer on identity, autonomy, and continuity. A well-built agentic agent likely uses one or more LLM agents inside its reasoning loop.

🚀 Summary

LLM Agents are practical, modular systems designed to accomplish tasks via reasoning and tools. Agentic Agents are more ambitious: persistent, self-guided systems that simulate long-term autonomy. While LLM agents form the backbone of many agentic designs, the agentic layer adds memory, adaptability, and personality — making them feel more like intelligent digital beings.