AI Agents Engineering Course:Build AI Agents with FastAPI, Python, LangChain, RAG, N8N, MCP & Docker – Live Training
(Master AI-Assisted Coding, Prompt Engineering, Agentic AI, RAG, FastAPI, n8n, MCP & Production Deployment)
Master the future of AI application development with our Agentic Developer Course: Build AI Agents from Scratch with FastAPI, LangChain, RAG, MCP & Docker — a hands-on live training program designed for beginners and professionals who want to build modern, intelligent applications from the ground up.
You will learn Generative AI, Agentic AI, Prompt Engineering, Python, n8n, LangChain, RAG, Streamlit, FastAPI, Pydantic, MCP, and LangSmith through practical projects rather than passive theory. The course takes you from LLM fundamentals, prompts, APIs, JSON, and tool calling to building Telegram AI assistants, Jira test case workflows, search agents, RAG applications, and advanced agent orchestration.
The program concludes with production-oriented development covering Docker, Git, GitHub, structured outputs, tracing, human approval workflows, and deployment. You will build the AI IT Helpdesk Agent capstone, combining Streamlit UI, FastAPI backend, LangChain orchestration, RAG, MCP tools, ticket management, LangSmith tracing, and Docker to create a complete end-to-end agentic AI application.
About the Instructor:
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Shashank Gupta is an experienced Engineering Leader, AI Builder, Automation & Agentic AI Practitioner with 13+ years of experience across automation, software quality, engineering strategy, and AI-driven solutions. He focuses on applying Generative AI and Agentic AI to practical engineering and business workflows, helping learners understand how AI can be integrated into real-world applications. His industry experience and practical approach make his training focused on real implementation rather than learning frameworks in isolation. Shashank delivers practical training focused on Agentic AI, AI application development, Python, LangChain, RAG, FastAPI, Streamlit, MCP, Docker, and LangSmith. His approach follows a progressive learning path where learners first understand workflows visually and then move into Python, LangChain, AI agents, retrieval, tools, backend APIs, UI development, tracing, and deployment patterns. The training emphasizes understanding how different components work together to build complete agentic applications. With 30+ training programs delivered and 200+ professionals mentored, Shashank brings a hands-on and structured approach to technical learning. His sessions focus on simplifying complex AI concepts and connecting them to practical development scenarios. Through guided projects and real application builds, he helps learners develop the confidence to build, explain, debug, and improve agentic AI applications using modern AI engineering practices. |
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 109 USD or 25000 INR 8,900 Rupees
OR
Free Demo On:
Indian Timings: 17th September @ 9 PM – 10 PM (IST)/
U.S Timings: 17th September @ 11:30 AM – 12:30 PM (EST)/
U.K Timings: 17th September @ 4:30 PM – 5:30 PM (BST)
Class Schedule :
| Days | India Timings (IST) | US Timings (EST) | UK Timings (BST) |
| Demo | 17th September @ 9 PM – 10 PM (IST) | 17th September @ 11:30 AM – 12:30 PM (EST) | 17th September @ 4:30 AM – 5:30 PM (BST) |
| Day 1 | 18th September @ 9 PM – 10 PM (IST) | 18th September @ 11:30 AM – 12:30 PM (EST) | 18th September @ 4:30 AM – 5:30 PM (BST) |
| Day 2 | 21st September @ 9 PM – 10 PM (IST) | 21st September @ 11:30 AM – 12:30 PM (EST) | 21st September @ 4:30 AM – 5:30 PM (BST) |
| Day 3 | 23rd September @ 9 PM – 10 PM (IST) | 23rd September @ 11:30 AM – 12:30 PM (EST) | 23rd September @ 4:30 AM – 5:30 PM (BST) |
| Every Monday, Wednesday & Friday — 1-hour sessions will go on | Every Monday, Wednesday & Friday — 1-hour sessions will go on | Every Monday, Wednesday & Friday — 1-hour sessions will go on |
What Students Say about the trainer:
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⭐ The trainer explained AI Agents Engineering concepts in a very simple and practical way. I learned how to build AI agents using Python, FastAPI, LangChain, RAG, and n8n with real-time examples. The hands-on sessions and practical projects made the learning experience excellent. – Kumar ⭐ Excellent course for learning Agentic AI and AI Application Development. The trainer clearly explained LLMs, Prompt Engineering, LangChain, tool calling, RAG, and MCP with practical examples. The real-world projects helped me understand how AI agents work and how to build useful AI applications. – Priya ⭐ The practical sessions and real-world projects made learning AI agents and agentic workflows very easy. Building applications with n8n, LangChain, RAG, and Streamlit gave me a strong understanding of how different AI components work together. – Joshi ⭐ One of the best industry-focused training programs I have attended. The course covered Python, FastAPI, LangChain, RAG, MCP, Docker, and AI agent development in a practical way. The trainer supported us throughout the projects and explained how to build complete AI applications with UI, backend APIs, tools, retrieval, and deployment. The learning environment was interactive and professional. – Sneha ⭐ The trainer provided clear explanations and real-world examples throughout the course. I gained strong knowledge in Agentic AI, LangChain agents, tool calling, RAG, MCP, FastAPI, and AI application development. The hands-on approach helped me understand how to build and deploy practical AI agent solutions. – Vijaya |
Who can enroll for this course?
- Students & Fresh Graduates interested in Generative AI and Agentic AI Development
- Python Developers looking to build AI Agents, RAG Applications, and AI-powered solutions
- QA & Automation Engineers who want to apply AI to automation, testing, and intelligent workflows
- Software Engineers interested in LangChain, FastAPI, Tool Calling, and Agent Orchestration
- DevOps & Cloud Professionals planning to explore Docker, AI Application Deployment, and Automation
- Beginners with basic programming knowledge who want to learn AI Application Development from the fundamentals
- Technology Professionals interested in n8n, Streamlit, Supabase, MCP, LangSmith, and RAG
- Anyone passionate about building Intelligent AI Agents, Automation Workflows, and Production-Ready AI Applications
Salient Features:
- 30+ Hours of Live Training along with recorded videos
- 1Year access to the recorded videos
- Course Completion Certificate
What will I learn by the end of this course?
- Understand Generative AI & Agentic AI including LLM fundamentals, tokens, context, hallucinations, prompts, workflows, and agent concepts.
- Build AI Workflows with n8n using triggers, memory, credentials, Telegram integrations, RAG workflows, and external tools.
- Develop AI Applications with Python using functions, JSON, APIs, project structures, environment variables, packages, and type hints.
- Build AI Agents with LangChain using prompts, chains, tools, function calling, routing, orchestration, and multi-agent workflows.
- Create RAG Applications using document processing, chunking, embeddings, vector search, metadata, Supabase pgvector, and grounded responses.
- Develop AI Interfaces & Backend APIs using Streamlit and FastAPI to build interactive AI applications and backend services.
- Implement MCP & AI Tool Integration by creating MCP servers, tools, resources, and prompts for connecting AI agents with external capabilities.
- Build & Deploy Production-Ready AI Agents using Pydantic validation, LangSmith tracing, Git, GitHub, Docker, human approval workflows, and cloud deployment.
Course syllabus:
Module 1: Agentic AI Foundations for Builders
- Understanding what makes an AI system agentic
- Goals, runtime decisions, tools, memory and the ability to act
- Workflow vs Agent
- Agent Loop and ReAct
- Agents that classify, route, retrieve and update records
- Understanding when agents ask for help
Module 2: LLM Behavior, Prompts & API Mechanics
- Understanding model behavior through live notebook demonstrations
- Token splitting and context cutoffs
- Understanding hallucination examples
- Prompt roles and structured instructions
- JSON output
- Safe API-key handling
Module 3: Visual Agent Prototyping with n8n
- Building workflows with n8n
- Triggers that start workflow execution
- Credentials for connecting external systems
- Nodes for transforming JSON
- AI Agent steps for generating responses
- Memory, Telegram and webhooks as user-facing interfaces
Module 4: Python Bridge for AI Applications
- Moving from notebook cells into small project folders
- Python functions and imports
- Working with files, pathlib and JSON
- Using dotenv and packages
- Virtual environments
- Git-safe project structure for LangChain and RAG code
Module 5: Advanced LangChain Application Development
- Building reusable prompt-model-parser chains
- Building agents that choose tools
- Tool calling
- Agent routing and orchestration
- Multi-agent workflow patterns
- Using LangSmith traces to reveal hidden steps
Module 6: End-to-End UI & Backend Applications
- Turning Python scripts into browser applications and backend services
- Building Streamlit interfaces
- Building FastAPI endpoints
- Request/response contracts and validation flows
- Tavily search tools and source-backed answers
- Trace inspection
Module 7: RAG for Private & Document Knowledge
- Building retrieval before generation
- Separating ingestion and answering into pipelines
- Chunk size and overlap
- Embedding generation
- Supabase pgvector storage and semantic retrieval
- Citations and grounded answers
Module 8: Docker, Structured Outputs & Productionizing Agents
- Running n8n locally with Docker
- Verifying containers and volumes
- Working with webhooks and API exercises
- Making model output predictable with Pydantic schemas
- Using Literal choices, retries and fallbacks
- Safe production boundaries
Module 9: MCP, Backend Deployment & Capstone Engineering
- Exposing capabilities through MCP tools, resources and prompts
- GitHub and Docker
- Streamlit UI deployment
- FastAPI backend deployment patterns
- Complete AI IT Helpdesk Agent with retrieval, tools, approval and tickets
Agentic Developer Application Engineering:
- Application Inputs
- Telegram Messages
- Streamlit Forms
- Uploaded Documents
- Webhooks
- API Requests
- Google Sheets Records
- Business-Support Issues
- Application Reasoning
- System Prompts
- Constraints
- Context Engineering
- ReAct Loops
- Tool Choice
- Structured Triage
- Model Behavior Under Limited Context
- Application Knowledge
- Private Documents
- Product Records
- Helpdesk Procedures
- Page Inventories
- Chunks & Metadata
- Embeddings
- Supabase Vector Retrieval
- Application Actions
- Python Functions
- FastAPI Endpoints
- LangChain Tools
- MCP Tools
- Ticket Creation & Lookup
- Comments
- Google Integrations
- Controlled Communication Paths
- Application Safety
- Pydantic Validation
- Literal Choices
- Safe Fallbacks
- Approval Before Writes
- Secret Hygiene
- LangSmith Trace Inspection
- Clear Tool Boundaries
- Application Delivery
- Project Folders
- Dependencies
- Docker Packaging
- Git Commits
- GitHub Repositories
- Streamlit/FastAPI Deployment Secrets
- Local Checks
- Public App Verification
Technologies covered across the Agentic Developer Course:
- n8n and automation
- Python, FastAPI and app structure
- Advanced LangChain and agent tooling
- RAG and vector retrieval
- MCP application layer
- End-to-end deployment workflow
Applications & Real-World AI Agent Projects
- Jira to Test Case Workflow
- Telegram AI Assistant
- Support Triage Agent
- Streamlit Search Agent
- n8n RAG Agent
- DocuChat RAG Assistant
- Employee MCP Server
- UI + FastAPI Backend Agent
- AI IT Helpdesk Agent
End-to-end UI, backend and agent architecture:
- Streamlit and User-Facing UI
- FastAPI Backend Services
- LangChain Orchestration
- Chains, Tools & Function Calling
- Routing & Retrieval
- MCP & Tool Standardization
- LangSmith Tracing & Debugging
- Docker & Deployment Readiness
- Environment Variables & Cloud Secrets
- GitHub & Application Deployment
AI IT Helpdesk Agent – Final Capstone
- Streamlit UI
- FastAPI Backend
- Pydantic Metadata
- LangChain Orchestration
- RAG Procedure
- MCP Ticket Tool
- LangSmith Trace
- Docker Deployment
- Password Reset, VPN Failure, WiFi & Outlook Support
- Request Validation & Approved Documentation Retrieval
- System Status Checking & Recommended Next Steps
- Human Approval Before Ticket Creation
- Production-Ready FastAPI, LangSmith & Dockerized Deployment