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🧠 Day 2 Agentic AI Quiz: LLM vs Agent | Medium Level

Medium Level 🧠 Medium Quiz: LLM vs Agent – Conceptual Understanding 🔎 Deepen your understanding of Day 2 concepts 🎯 Learn how LLMs differ from agents in architecture and functionality 🔁 Explore ...

Agentic AImedium
5Questions6Minutes60%To Pass0Attempts

Study Material: Agentic AI

Day 2: LLM vs Agent – What’s the Real Difference?
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Before You Start

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Duration

6 minutes

Questions

5 total

Pass score

60% required

Marking

No negative marking

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What to expect

  • You have 6 minutes to complete this quiz.
  • The quiz contains 5 questions.
  • You need 60% or higher to pass.
  • You can navigate between questions using the question grid.
  • The timer cannot be paused once the quiz starts.
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🤖 Day 6 of Agentic AI Series ---------------------------- Tools and Tool-Calling in Agentic AI 🔧 An AI agent becomes truly powerful when it can DO things, not just talk. This is where tools and tool-calling come into play. Let’s understand how tools work in Agentic AI 👇 🧠 What Are Tools in Agentic AI? Tools are external functions or systems that an AI agent can use to perform actions beyond text generation. Examples of tools: - APIs - Databases - Search engines - Code execution - File systems Tools allow agents to interact with the real world. 🔗 What Is Tool-Calling? Tool-calling is the process where an AI agent: - Understands the task - Chooses the right tool - Sends correct inputs to the tool - Receives the output - Uses the result to decide the next step This happens automatically inside the agent loop. 🔄 Agent Loop with Tools Goal → Plan → Choose Tool → Execute Tool → Observe Result → Update Plan This loop makes the agent action-oriented. 📌 Example: Customer Support Agent Goal: Resolve a refund request Steps: 1. Read user query 2. Fetch order details using API 3. Check refund eligibility 4. Initiate refund via payment service 5. Confirm with the user Without tools, this agent could only reply — not resolve. ⚠️ Challenges with Tool-Calling - Incorrect tool selection - Invalid inputs - API failures - Security and permission issues Modern systems use validation, retries, guardrails, and human approval. 💡 Why Tools Matter Tool-enabled agents can: - Automate workflows - Perform real business actions - Reduce manual effort - Scale intelligent operations

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🧠 Day 5 Agentic AI Quiz: Memory Types in Agentic AI 🧠

🤖 Day 5 of Agentic AI Series ---------------------------- Memory Types in Agentic AI 🧠 An AI agent is only as good as what it remembers. Memory is what allows agents to learn, adapt, and improve over time. Let’s understand the different types of memory in Agentic AI 👇 🧠 What Is Memory in Agentic AI? Memory enables an AI agent to: - Remember past interactions - Retain important context - Avoid repeating mistakes - Personalize future actions Without memory, agents behave like stateless chatbots. 🔹 Types of Memory in Agentic AI 1️⃣ Short-Term Memory - Stores recent conversations or actions - Exists only during the current session - Limited in size Example: Remembering the user’s last question or task in progress. 2️⃣ Long-Term Memory - Stores information across sessions - Helps the agent learn over time - Used for personalization and history Example: User preferences, previous project details, or recurring tasks. 3️⃣ Episodic Memory - Stores past experiences as events - Helps agents recall what happened and when - Useful for reasoning and learning Example: “Last time the API failed at step 3, retry with a different approach.” 4️⃣ Semantic Memory - Stores facts, rules, and knowledge - Independent of specific experiences - Used for decision-making Example: Knowing that an API requires authentication. 🔄 How Memory Works with Planning Memory feeds into planning by: - Providing context - Informing decisions - Improving action selection Better memory = smarter plans. ⚠️ Challenges with Memory - Storing irrelevant data - Forgetting important context - Memory size and cost - Data privacy concerns Modern agents use filtering, summarization, and vector databases to manage memory efficiently. 💡 Real-World Use Cases - Personal AI assistants - Customer support bots - Autonomous workflow agents - Recommendation systems

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