Agentic Agents FAQ: Top Questions

Building a true Agentic Agent requires more than just calling a large language model (LLM) with prompts. It involves designing an architecture where the agent can plan, remember, act, observe, and reflect — just like an intelligent software entity.

Agentic agents are modular systems. Each module handles a distinct cognitive function such as memory, planning, execution, or learning. This modularity makes them flexible and upgradable but also introduces complexity in coordination.

🧩 Core Components of an Agentic Agent

• 1. Language Model (LLM Core): Powers reasoning, interpretation, instruction-following, summarization, and generation.
• 2. Memory: Stores long-term and short-term contextual data. Includes episodic memory (past events) and semantic memory (structured knowledge).
• 3. Planner: Translates goals into step-by-step subgoals or executable plans, often using tools like ReAct, CoT, or scratchpads.
• 4. Tool Execution Interface: Connects the agent to APIs, databases, web tools, scripts, etc., allowing actions beyond pure text.
• 5. Reflector or Critic: Allows the agent to assess its own progress and outputs, sometimes rewriting its plan or strategy.
• 6. State Tracker: Tracks current status, completed tasks, pending goals, and contextual changes over time.
• 7. Scheduler/Event Loop: Runs the agent periodically, manages goal queues, and triggers re-evaluation cycles.

🧠 Diagram of a Typical Agentic Loop

initialize(agent)
while agent.active:
    observe_environment()
    recall_memory()
    plan_next_actions()
    act_via_tools()
    evaluate_and_reflect()
    store_new_memories()

⚙️ Modular Example Setup (Minimal)

• LLM: OpenAI GPT-4 (via API)
• Memory Store: ChromaDB + vector embeddings
• Planner: LangChain ReAct agent with scratchpad
• Executor: LangChain ToolChain + API tools
• Reflection: Self-debug prompt template with feedback loop
• Loop: Cron job or scheduler to simulate ongoing "life"

📚 Real-Life Analogy

Imagine you are building a virtual research assistant. The assistant:

• Uses its memory to recall which papers you liked before
• Plans a research path with subtopics to explore
• Uses tools like Google Scholar to fetch articles
• Summarizes and critiques papers for you
• Stores your preferences and results for future tasks
• Reflects on whether you seem interested in a topic (based on time spent or feedback)

🔍 Why Each Module Matters

• LLM alone is not enough: It cannot remember context or track goals.
• Planning without tools: Limits the agent to passive tasks.
• No memory: Means the agent has no growth or continuity.
• No reflection: Makes the agent prone to repetition or errors.

🛠️ Tools and Frameworks to Build This

• LangChain Agents: Easily integrate planning, memory, and tools.
• CrewAI: For multi-agent coordination and role-based tasking.
• Semantic Kernel: Provides planning, memory, and "skills".
• OpenAgents: Designed for real-world APIs and reproducible agents.
• ReAct / AutoGPT: Popular planning loop paradigms.

📘 Summary

Agentic agents are systems composed of interdependent parts. Each component enhances the agent's ability to not only perform tasks but to behave like an autonomous, intelligent actor. The integration of memory, planning, and reflection distinguishes agentic agents from simple LLM tools.