Ai Agent Engineering: Build Production Ready Agentic Systems
[center]
Ai Agent Engineering: Build Production Ready Agentic Systems
Published 2/2026
Created by Arnab Das
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 45 Lectures ( 8h 16m ) | Size: 4.44 GB [/center] AI Agent Engineering: Build Production Ready Agentic Systems with LangChain, MCP, FastAPI What you'll learn
✓ Understand agentic systems and clearly differentiate them from traditional LLM applications.
✓ Design AI agent architectures using modern agentic patterns, memory, and tool based reasoning.
✓ Build production AI agents with LangChain, LangGraph, Deep Agents, MCP, RAG pipelines, and multi-agent collaboration.
✓ Secure, evaluate, and monitor AI agents using guardrails, Langfuse, observability, authentication, and performance metrics.
✓ Deploy scalable agentic systems to the cloud using Docker, FastAPI, and real world production workflows. Requirements
● Basic Python programming knowledge (functions, classes, virtual environments)
● Familiarity with REST APIs and JSON
● Fundamental understanding of LLMs or prior experience using ChatGPT or similar tools
● A laptop or desktop with internet access
● Willingness to install Python, VS Code, Pycharm and required libraries Description
AI agents are rapidly evolving from simple chatbots into powerful autonomous systems capable of reasoning, planning, using tools, collaborating with other agents, and operating on real world enterprise data. Modern organizations are increasingly adopting agentic systems to automate workflows, enhance decision making, and build intelligent products.
This course is designed to help you become an AI Agent Engineer.
In AI Agent Engineering: Build Production Ready Agentic Systems, you will learn how to design, build, secure, and deploy modern AI agents using LangChain, MCP (Model Context Protocol), RAG pipelines, multi-agent architectures, and FastAPI. You will also gain a clear understanding of what agentic systems are and how they differ from traditional LLM based applications.
Unlike many introductory AI courses, this program focuses on real engineering practices. You will move beyond prompt based demos and explore professional agent architectures, including planning, memory, tool execution, and agent collaboration. You'll build MCP servers to connect agents with external tools and context, implement retrieval augmented generation for enterprise knowledge, and create scalable APIs for your agents using FastAPI.
The course emphasizes production readiness. You will learn how to add guardrails, observability, authentication, and evaluation to your agents, helping you build systems that are reliable, secure, and maintainable. Through hands on projects, you will develop autonomous task agents, enterprise knowledge agents, and multi-agent workflows that reflect real industry use cases.
By the end of this course, you won't just understand how AI agents work, you'll know how to engineer complete agentic systems, deploy them to cloud environments, and operate them in production.
Whether you are a software developer, backend engineer, full-stack developer, or AI practitioner, this course will give you the practical skills needed to move from basic LLM applications to production grade autonomous systems.
If you're ready to go beyond chatbots and start building real world AI agents, this course is your complete guide. Who this course is for
■ Software developers and backend engineers who want to build real world AI agents and agentic systems
■ Full-stack developers looking to add production-grade AI agent skills to their toolkit
■ AI/ML practitioners who want hands-on experience with LangChain, MCP, RAG, and multi-agent architectures
■ Technical architects interested in designing scalable, secure agentic systems
■ Developers moving beyond basic LLM apps and chatbots into autonomous AI systems
■ Professionals preparing for advanced AI engineering or agent architecture roles
Sitemiz, hukuka, yasalara, telif haklarına ve kişilik haklarına saygılı olmayı amaç edinmiştir. TR AKDAG web sitesi hayvan severlerin oluşturduğu bir platformdur. sitemizde kumar, bahis vb. yasadışı faaliyetlerin sağlanması söz konusu değildir. trakdag.com daki videolar internet ortamında yayın yapan diğer video sitelerinden alıntıdır. Sitemiz, 5651 sayılı yasada tanımlanan "yer sağlayıcı" olarak hizmet vermektedir. 5651 Sayılı kanun’un 8. maddesine ve T.C.K’nın 125. maddesine göre TÜM ÜYELERİMİZ yaptıkları paylaşımlardan sorumludur. İlgili yasaya göre, site yönetiminin hukuka aykırı içerikleri kontrol etme yükümlülüğü yoktur. Bu sebeple, sitemiz "uyar ve kaldır" prensibini benimsemiştir. sitemiz de sansürlenmemiş içerik yoktur. telif hakkına konu olan eserlerin yasal olmayan bir biçimde paylaşıldığını ve yasal haklarının çiğnendiğini düşünen hak sahipleri veya meslek birlikleri, hakkında admin@trakdag.com mail adresinden bize ulaşabilirler. Buraya ulaşan talep ve şikayetler tarafımızdan incelenerek, şikayet yerinde görüldüğü takdirde ihlal olduğu düşünülen içerikler sitemizden kaldırılacaktır. ayrıca, mahkemelerden talep gelmesi halinde hukuka aykırı içerik üreten ve hukuka aykırı paylaşımda bulunan üyelerin tespiti için gerekli teknik veriler sağlanacaktır.
TR KANGAL AKDAG Media
Ai Agent Engineering: Build Production Ready Agentic Systems
![[Resim: b29dea20c7dfe8e2fa2ee86e028eafe8.jpg]](https://i127.fastpic.org/big/2026/0215/e8/b29dea20c7dfe8e2fa2ee86e028eafe8.jpg)