Agentic Agents FAQ: Top Questions

While both Agentic Agents and LLM Agents leverage large language models (LLMs) at their core, they are conceptually and architecturally distinct. LLM Agents are task-centric, typically reactive to prompts and contextually narrow. Agentic Agents, by contrast, are designed to simulate human-like agency — including autonomy, memory, initiative, and sustained goal pursuit.

You can think of an Agentic Agent as a more evolved LLM agent: it doesn’t just *respond*, it *acts, remembers, adapts*, and *reflects*. This fundamental shift brings new capabilities — and also new responsibilities — in system design.

🔍 Core Differences

• Autonomy: LLM Agents require human prompts to initiate. Agentic Agents can operate independently with internal drives or goals.
• Persistence: LLM Agents usually exist per-session. Agentic Agents persist across time, remembering and adapting as they go.
• Memory: LLM Agents use memory for context. Agentic Agents use memory for identity, planning, learning, and decision-making.
• Goal Formulation: LLM Agents complete instructions. Agentic Agents can set, revise, and abandon goals based on feedback or priorities.
• Reasoning Scope: LLM Agents are good at one-shot reasoning. Agentic Agents engage in multi-step, long-horizon planning and reflection.

📊 Summary Comparison Table

Aspect	LLM Agent	Agentic Agent
Initiation	Human-prompted	Self-initiated or reactive
Time Scope	Single session	Continuous or multi-session
Memory Type	Short-term or local	Episodic + semantic + evolving
Planning	Prompt-scoped	Recursive, revisable, long-term
Adaptability	Low (without toolchain)	High (adaptive reasoning loop)
Tool Use	Triggered by user flow	Integrated into goal-driven behavior

📘 Example Scenario

Suppose you're building an AI project assistant:

• LLM Agent: You say “summarize this meeting,” and it returns a summary. Next time, it starts fresh.
• Agentic Agent: It proactively reminds you to schedule a follow-up, updates its internal understanding of the project timeline, and remembers past blockers from previous meetings.

🧠 Conceptual Shift

Moving from LLM Agents to Agentic Agents is like moving from a calculator to an intern. The intern may be slower and more complex to manage, but they can plan, learn, and take initiative. With Agentic Agents, we begin to approximate AI systems that feel more like collaborators than tools.

⚠️ Challenges of Agentic Agents (vs. LLM Agents)

• Need for persistent and reliable memory systems
• Greater infrastructure and orchestration complexity
• Harder to evaluate due to emergent behaviors
• More risk of goal drift or unintended autonomy

🔧 Development Notes

• LLM Agents can be created with LangChain tools and ReAct prompts
• Agentic Agents require event loops, memory buffers, planning graphs, and policy frameworks
• Realistic deployment involves schedulers, vector DBs, and sometimes state machines

🧩 Conclusion

LLM Agents are reactive task workers. Agentic Agents are proactive collaborators. Understanding the distinction is essential as we build toward more intelligent, personalized, and adaptive AI systems.