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Unclaimed ProfileThe Deep Agent course by KGP Talkie provides a comprehensive deep dive into the evolving ecosystem of AI agents, moving beyond simple chatbots to architecting complex, autonomous multi-agent systems. This curriculum is meticulously designed for developers who want to harness the power of LangChain v1, Google Gemini 3, and the Model Context Protocol (MCP) to solve real-world automation challenges. By integrating advanced tools like Qdrant for vector retrieval and Docling for sophisticated multimodal document processing, the course addresses the core difficulty of handling unstructured data such as dense financial reports, complex tables, and high-resolution images. Students gain practical experience in building production-ready architectures that leverage hybrid search, memory management, and cost-optimized context caching. What sets this course apart is its emphasis on multi-agent collaboration, teaching learners how to orchestrate specialized roles like research and editor agents to automate multi-step workflows. Whether you are looking to build autonomous research bots or enterprise-grade data extraction pipelines, this program bridges the gap between theoretical RAG concepts and the reality of deploying scalable, multi-modal AI agents in modern cloud environments using Docker.
About the creator
KGP Talkie | Laxmi Kant
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Laxmi Kant, widely recognized online as the creator of KGP Talkie, is an experienced educator and software engineer specializing in artificial intelligence, machine learning, and modern backend integration. With a passion for demystifying cutting-edge technology, Laxmi focuses heavily on practical, hands-on tutorials that bridge the gap between academic theory and deployment-ready software. His teaching methodology revolves around building real-world projects, which is evident in his extensive curriculum covering LangChain, LangGraph, Ollama, FastAPI, and Model Context Protocol. Laxmi believes that the best way to master artificial intelligence is through direct implementation. He guides students through the step-by-step process of designing private…Show more
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Program Overview
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Price may change · updated within 1–2 weeks
Course language
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Build production-ready AI agents using Google Gemini, LangChain v1, MCP, and modern agent design patterns.
- Design and implement multimodal RAG pipelines using Docling, Gemini, Qdrant vector database, and hybrid search.
- Process PDFs, tables, and images at scale using Docling, Docker, and structured data extraction techniques.
- Implement hybrid search, re-ranking, memory, MCP tools, and cost-optimized context caching in real AI systems
- Create autonomous multi-agent research systems with orchestrator, researcher, and editor agents for finance use cases.
Best For
- AI engineers looking to transition from basic RAG to multi-agent autonomous systems.
- Developers focused on building scalable, production-ready AI applications using Google Gemini.
- Data professionals who need to parse complex documents like PDFs and financial tables at scale.
- Tech practitioners aiming to master LangChain v1 and Model Context Protocol (MCP) design patterns.
Not For
- Complete beginners with zero prior experience in Python programming or foundational AI concepts.
- Individuals seeking a purely theoretical overview without hands-on coding or implementation projects.
- Learners strictly interested in low-code or no-code AI tools without exposure to backend infrastructure.
- Professionals who exclusively work with legacy tech stacks and have no interest in modern vector database architectures.
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