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Unclaimed ProfileAgentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3 offers a comprehensive deep dive into the rapidly evolving landscape of autonomous AI systems. This course moves beyond basic chatbot development, focusing instead on the sophisticated architecture required to build functional, reasoning-based AI agents. Participants will master the integration of Google’s latest Gemini 3 models with LangChain and LangGraph to construct systems capable of performing multi-step tasks, tool orchestration, and complex reasoning. A standout feature of this curriculum is its emphasis on modern integration standards like the Model Context Protocol (MCP), which bridges the gap between AI models and real-world data sources. Beyond the AI logic, the course provides a crucial bridge to production engineering. By utilizing FastAPI to wrap agentic workflows and deploying these applications onto AWS EC2 instances, learners gain the technical proficiency needed to move projects from a local development environment into scalable, enterprise-grade cloud deployments. The inclusion of safety guardrails, human-in-the-loop approval processes, and sandboxed execution environments ensures that students learn how to manage the inherent risks of autonomous agents, making this an essential program for developers aiming to build secure, reliable, and intelligent AI applications that truly deliver business value.
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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Learning format
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Price may change · updated within 1–2 weeks
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What You'll Learn
- Build real AI agents using LangChain and Google Gemini that can reason, use tools, and complete tasks autonomously
- Design agent architectures using ReAct patterns, tool calling, and structured decision making
- Implement short-term and long-term memory in AI agents using databases and embeddings for personalized experiences
- Create and manage agent tools for web search, weather, finance, document analysis, and external APIs
- Apply prompt engineering techniques to control agent behavior, improve output quality, and guide tool usage
- Add safety layers such as guardrails, human-in-the-loop approval, and middleware controls to prevent errors and misuse
- Stream real-time responses and generate structured outputs from AI agents in production-style applications
- Secure AI agents with sandboxed code execution to prevent file deletion, credential leaks, and system risks
- Build REST APIs for AI agents using FastAPI with validation, CORS, and production-ready patterns
- Develop full-stack AI applications using Streamlit connected to LangChain agents
- Deploy AI agents on AWS EC2 and configure them for real-world access and scalability
Best For
- Software developers looking to transition into AI and LLM orchestration roles
- Backend engineers aiming to integrate production-ready AI agents into existing REST APIs
- Cloud developers interested in deploying complex GenAI applications on AWS
- Data scientists who want to move beyond simple prompts to autonomous agent systems
Not For
- Total beginners with zero experience in Python programming
- Professionals looking for a high-level conceptual overview without hands-on coding
- Developers strictly focused on UI/UX frontend design rather than backend agent architecture
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