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Hello, and welcome to this course on AI agents.

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The demand for AI agent developers is growing fast as more businesses turn to intelligent

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systems that can reason, take action, and solve problems in real time.

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From customer support chatbots that handle complex queries, to research assistants that

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fetch and summarize data, to legal assistants that analyze case law, medical agents that

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provide diagnostic suggestions, and tools that generate visualizations or write reports,

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AI agents are becoming a critical part of modern applications.

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This rising need has opened up exciting opportunities for developers who know how to connect language

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models with tools, data sources, and logic.

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Companies are actively seeking talent that can go beyond basic prompts and build smart,

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task-driven solutions.

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Now is the perfect time to get started.

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This hands-on course is designed for aspiring software engineers, data scientists, machine

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learning engineers, AI architects, automation engineers, or someone in a related role.

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Python programming skills and experience are essential for this course, as you will immediately

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start building AI agents.

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Familiarity with core AI concepts and the chain framework is highly recommended.

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This course begins with a detailed look at AI agents, covering what they are, how they

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differ from traditional workflows, and when to best deploy them.

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You'll be provided with a foundation in tool calling and chaining using LangChain.

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You'll explore why language models need tools and how function calling increases precision.

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Through guided videos and readings, you'll build a math assistant by converting the functions

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into tools and orchestrating them with LangChain.

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You'll also be introduced to LangChain's built-in agents.

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Module 2 introduces you to LCEL, or LangChain Expression Language, a concise, chain-first

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syntax that simplifies the creation of modular AI workflows.

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You'll learn when and how to manually invoke tools based on LLM outputs, and how to parse

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and validate these calls for structured execution.

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In a hands-on lab, you'll build a tool calling agent to automate U2 operations.

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In Module 3, the course focuses on building with LangChain's built-in agents.

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You'll learn how to create natural language data visualizations and build a conversational

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agent that queries SQL databases using plain English.

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With detailed readings and walkthroughs, you'll implement two agents, one for data visualization

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and another for conversational database access, using LangChain's built-in components.

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To get the most from this course, make sure you watch the videos, go through each reading,

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check your understanding with practice quizzes, perform the hands-on labs, and test your knowledge

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with the graded assessment.

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Thank you and all the best with this course!