MASTER OF AI AGENTS + Certificate
Lesson 3.5: Testing and Refining Your Agent
MASTER OF AI AGENTS + Certificate
0% Completed
Module 1: Introduction to AI Agents and LangChain
In this foundational module, learners are introduced to the concept of AI agents and how they go beyond traditional chatbots to perform autonomous, intelligent tasks. The lessons explore real-world applications of AI agents in business automation, including customer support, data handling, and digital operations.
Learners discover the capabilities of LangChain, the leading open-source framework for building LLM-powered agents. They also learn the differences between agents, chains, and tools, and how each part fits into building an intelligent system.
The module concludes with a hands-on environment setup, where learners install Python, LangChain, and their preferred LLMs (like OpenAI or Ollama), and run their first simple LLM prompt.
✅ By the end of this module, learners will:
• Clearly understand what an AI agent is and how it differs from a chatbot
• Recognize common use cases for agents in business automation
• Grasp the core components of LangChain and how agents are structured
• Set up their development environment and run their first LLM call
In Module 2, learners dive into the core building blocks of the LangChain framework, gaining the practical knowledge needed to create powerful language model–driven applications.
The module explains how LangChain connects LLMs, prompts, chains, memory, and tools into a unified workflow. Through hands-on examples, learners build their first LangChain chains and begin to understand how agents make decisions and use tools to complete tasks.
This foundational knowledge sets the stage for creating dynamic, intelligent AI agents in later modules.
✅ By the end of Module 2, learners will be able to:
• Understand the structure and purpose of LLMs, prompts, chains, memory, retrievers, and tools in LangChain
• Use PromptTemplate to create reusable prompt structures
• Build simple LLMChain workflows for single-step tasks
• Use memory to preserve context in conversations
• Add retrievers to access external knowledge (e.g., document search)
• Understand how tools connect LangChain to APIs, functions, or databases
🎯 Objective:
To guide learners through the hands-on process of creating their first functional AI agent using LangChain, including running models locally and enabling tool usage.
🔹 Lesson 3.1: Using Ollama to Run LLMs Locally
• Goal: Run open-source language models like LLaMA, Mistral, or DeepSeek on your own machine.
• Topics Covered:
◦ Installing Ollama (cross-platform setup)
◦ Pulling models (e.g., ollama pull mistral)
◦ Running a model and testing it via terminal
◦ Benefits: privacy, no API cost, offline use
🔹 Lesson 3.2: Setting Up LangChain Agent
• Goal: Build an AI agent that can receive user input and respond intelligently using a local or API-based model.
• Topics Covered:
◦ Initializing LangChain with a model (OpenAI or Ollama)
◦ Creating a basic ConversationAgent
◦ Using prompt templates to format instructions
◦ Testing agent with simple Q&A
🔹 Lesson 3.3: Adding Tools to the Agent
• Goal: Extend the agent's capabilities by integrating tools like a calculator, weather fetcher, or knowledge base.
• Topics Covered:
◦ Defining tools as Python functions
◦ Wrapping them using LangChain's Tool class
◦ Connecting multiple tools to the agent
◦ Agent reasoning to select appropriate tools based on user input
🔹 Lesson 3.4: Conversation Memory & Personalization
• Goal: Enable the agent to remember prior messages and improve conversation flow.
• Topics Covered:
◦ Using ConversationBufferMemory for short-term memory
◦ Setting up memory with the agent
◦ Example: remembering user name, previous questions
◦ Optional: summarizing long chats to save memory space
🔹 Lesson 3.5: Testing and Refining Your Agent
• Goal: Evaluate the behavior and responsiveness of your first AI agent.
• Topics Covered:
◦ Running the agent in a loop or basic chat interface
◦ Debugging tool calls and memory issues
◦ Prompt tuning for better agent performance
◦ Next steps: packaging and deploying your agent
🔧 Outcome by the End of Module 3:
You’ll have a fully working AI agent that can:
• Respond to user queries using a local or remote LLM
• Use real-world tools (like calculators or APIs)
• Hold short conversations with memory
• Be customized for your future projects
🎯 Goal
Create an AI agent that can accept user input and generate intelligent responses using a local model (via Ollama) or an API-based model (like OpenAI).
📘 Topics Covered
1. 🔌 Initializing LangChain with a Model
◦ Connected to OpenAI or a local LLM (e.g., Mistral via Ollama).
◦ Configured language model with required credentials or local runtime.
2. 🤖 Creating a Basic ConversationAgent
◦ Used LangChain’s AgentExecutor or LLMChain.
◦ Defined simple logic for question-answering.
3. 🧾 Using Prompt Templates
◦ Structured prompts to guide the model’s behavior.
◦ Made responses more accurate and aligned with user intent.
4. 🧪 Testing the Agent
◦ Ran interactive Q&A through terminal or script.
◦ Confirmed the agent responds contextually based on input.
✅ Outcome
By the end of this lesson, you built a foundational AI agent capable of handling text input and returning useful responses — forming the core of more advanced agents with memory, tools, or voice.
🎓 Module 10: Capstone Project – Build Your Own AI Agent
This final module gives learners the chance to demonstrate everything they've learned by building and showcasing a complete, functional AI agent.
It includes guided structure, support, and a submission format designed to help learners build a portfolio-worthy project they can share with employers, clients, or the course community.