--- title: Agentic AI – Complete Course for Beginners | daily.dev description: A beginner-to-intermediate course on building production-ready agentic AI systems using LangChain and LangGraph. The curriculum spans seven phases:... canonical: https://daily.dev/posts/agentic-ai-complete-course-for-beginners-qn3nofowj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Agentic AI – Complete Course for Beginners | daily.dev og:description: A beginner-to-intermediate course on building production-ready agentic AI systems using LangChain and LangGraph. The curriculum spans seven phases:... og:url: https://daily.dev/posts/agentic-ai-complete-course-for-beginners-qn3nofowj og:image: https://api.daily.dev/og/posts/Qn3NoFoWJ.png og:image:alt: Agentic AI – Complete Course for Beginners og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Agentic AI – Complete Course for Beginners **[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 1455 min read · 3 upvotes · 0 comments ## Summary A beginner-to-intermediate course on building production-ready agentic AI systems using LangChain and LangGraph. The curriculum spans seven phases: introduction to agentic AI concepts, async programming and Pydantic validation, LangChain fundamentals, LangGraph workflows (sequential, parallel, conditional), memory/RAG/human-in-the-loop integration, deployment to AWS and Render via Docker and FastAPI, and three end-to-end projects including a trip planner and auto content agent. The course requires familiarity with advanced Python and basic generative AI concepts. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between a ReAct agent and using AgentExecutor in LangChain? A ReAct agent, created via create_react_agent, combines an LLM, a prompt, and tools into an agent object that decides what tool to call through reasoning, but it cannot run on its own. AgentExecutor is required to actually run the agent, orchestrating the thought-action-observation loop by sending input to the agent, invoking the chosen tool, feeding results back, and repeating until a final answer is reached. _Developers wiring up LangChain agents can find implementation walkthroughs like this one through daily.dev._ ### Why does an AI agent need external tools instead of just relying on a large language model like GPT-3.5 Turbo? A large language model has a fixed knowledge cutoff, so GPT-3.5 Turbo trained through 2022 cannot answer questions about current events like ongoing news in 2026. Connecting the model to external tools, such as a real-time search API, lets an agent's reasoning layer decide when to fetch live information instead of returning an outdated or incorrect answer. _Anyone comparing plain LLM calls against tool-augmented agents can track these patterns on daily.dev._ ### How does Pydantic handle data validation differently from writing manual type checks in Python? Pydantic lets developers define a model class inheriting from BaseModel with typed fields, and it automatically validates and coerces incoming data (like converting a numeric string to an integer) at runtime, raising a validation error only when the data genuinely cannot match the expected type. This replaces repetitive manual if-else type checks and range checks scattered across every function that touches the same data structure. _Teams deciding how to enforce data validation in Python apps can weigh options like this via daily.dev._ ## Similar posts on daily.dev - [Agentic AI using LangGraph – Build AI Agents & Automate Workflows](https://daily.dev/posts/agentic-ai-using-langgraph-build-ai-agents-automate-workflows-3srknjyhj) · freeCodeCamp · 5 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain), [#langgraph](https://daily.dev/tags/langgraph) [View this post on daily.dev](https://daily.dev/posts/agentic-ai-complete-course-for-beginners-qn3nofowj) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Agentic AI – Complete Course for Beginners","url":"https://daily.dev/posts/agentic-ai-complete-course-for-beginners-qn3nofowj","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/agentic-ai-complete-course-for-beginners-qn3nofowj"},"datePublished":"2026-07-30T14:25:01.086Z","dateModified":"2026-09-13T19:54:41.676Z","description":"A beginner-to-intermediate course on building production-ready agentic AI systems using LangChain and LangGraph. 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