Building Agentic AI Applications with Large Language Models

These flashcards cover the main concepts related to building agentic AI applications using large language models as discussed in the lecture notes.

What are the key characteristics of agents in AI?

1. Autonomy: operate independently. 2. Perception: use sensors to interpret environment. 3. Decision-Making: process information to make decisions. 4. Action: interact with the environment. 5. Adaptation: learn and improve from experience.

What does the term 'Agentic AI' refer to?

Agentic AI refers to intelligent agents that can take actions autonomously to achieve desired goals.

What is the course objective regarding agents?

To understand how agents work, their primitives and paradigms, and to build agentic applications using frameworks and use-cases.

What programming language is required for this course?

Intermediate Python is required.

What is the significance of 'Language-Reasoning Systems' in the context of AI?

They enable models to process natural language inputs and generate coherent, contextually relevant outputs.

What is needed to create agentic systems?

An understanding of multi-agent orchestration, retrieval mechanisms, and knowledge graphs, along with resilience in production deployments.

What are common tools mentioned for building agents in AI?

Open-source LLM tools, LangChain, and NVIDIA NIM.

What are the prerequisites for taking this course?

Familiarity with Python and experience with LangChain are preferred.

What are the possible applications of agentic AI as suggested in the course?

Applications can range from autonomous decision-making systems to intelligent agents for various tasks in software.

What is an agent in AI?

An agent is an entity that can perceive its environment, make decisions, and act autonomously to achieve specific goals.

What does the 'Agent Loop' concept refer to?

The iterative process where agents interact with their environment, make decisions, and adjust actions based on observed outcomes.

How can agentic systems extend towards production?

By integrating robust frameworks, handling challenges, and refining use-cases for real-world applications.

What does 'LLM' stand for in the context of this course?

Large Language Model.

What is the role of 'Communication Protocols' in agentic systems?

They define how agents correspond, interact, and share information within their operational environment.

Why is it important for agents to adapt?

Adaptation allows agents to improve their performance and effectiveness by learning from past experiences and changing conditions.

What does 'Agentic Decomposition' refer to?

It refers to breaking down complex tasks into smaller, manageable actions or components for execution by agents.

What is an 'Event Loop' in an agent's operation?

A mechanism that continuously checks for events or changes in the environment, allowing agents to respond in real-time.

What challenges might multi-agent systems face?

Challenges include coordination between agents, handling simultaneous tasks, and ensuring efficient communication.

What framework is preferred for multi-agent orchestration in this course?

LangGraph is one of the preferred frameworks for managing multi-agent orchestration.

How do agents perceive their environment?

Agents use sensors or data inputs to gather information and interpret the state of their environment.

What is an important consideration for building agentic applications?

Ensuring agents can be deployed effectively while maintaining high performance and reliability.

What does 'Agentic Simulation' involve?

It involves creating a simulated environment to test agent functionalities and behaviors before real-world deployment.