Architecting Autonomy: Agentic AI Bootcamp

Description:

Join us for the Architecting Autonomy: Agentic AI Bootcamp, a transformative 4 weeks bootcamp designed for the “Builder Era”. This program equips corporate developers and architects with the skills to move beyond simple chatbots and into the realm of Autonomous Agentic Systems.

Throughout this intensive program, you will master “Vibe Coding” – the art of using AI-native IDEs like Cursor to build software at 10x speed-and dive into LangGraph and Pydantic AI to create stateful, reliable agents. We focus on the Model Context Protocol (MCP) to securely bridge the gap between the Large Language Models (LLMs) and your proprietary enterprise data. By the end of this bootcamp, you will have built a multi-agent system capable of research, decision-making, and execution, fully deployed with enterprise-grade security & observability.

Duration:

4 Weeks

Course Code: BDT 531

Learning Objectives:

After this course, you will be able to:

1. Docker Introduction: Basic introduction to Docker as it makes it easier to build Agentic AI systems.
2. Vibe Coding & AI Velocity: Master Agent development IDE, Python, and spec-driven deployment to automate 80% of boilerplate coding .
3. Stateful Agents with LangChain/LangGraph: Move beyond linear chains to build cyclical state-machine-based agents with persistent memory and human-in-loop approvals.
4. Pydantic Agents with Pydantic AI: Leverage strict type-safety, dependency injection, and Pydantic structures for production-grade agentic behavior
5. The MCP Revolution: Build custom servers to connect LLMs directly to your SQL databases, local files, and internal APIs without exposing data to the public web.
6. Multi-Agent Orchestration: Use to tools like n8n, LangFlow to manage a “digital workforce” where specialized agents collaborate on complex business goals.
7. Enterprise RAG & Search: Optimize document retrieval using hybrid search, advanced chunking, and vector databases (Pinecone, Supabase, Chroma). (RAG: Retrieval Augmented Generation)
8. Local AI and Sovereignty: Deploy high-performance models like DeepSeek, Llama locally using Docker for total data privacy.
9. Voice and Vision Automation: Build real-time voice assistants and automated media pipelines.
10. AI Security & Compliance: Practical training on the EU AI Act, preventions for prompt injection, and data poisoning defense.

By the end of this AI Bootcamp, you will have the confidence and competence to tackle building Agentic AI systems.

This bootcamp is designed for individuals with a strong interest in AI and a desire to build AI Agents into existing corporate infrastructure. It might include Technical Leads, Software Engineers, and Solution Architects, QA Engineers.

1. One or more years technical experience
2. Programming experience with Python is must.
3. Basic understanding of using Large Language Models, including prompting

Course Outline:

Foundational Python for Agentic AI

• Pydantic Library
  • Structured inputs/outputs for tools, agents, and workflows
  • Typed schemas for message passing
  • Models, fields, validators and using BaseModel
• AsyncIO Fundamentals
  • Event loops, tasks, coroutines, and concurrency primitives
  • Running parallel tool calls, API requests

Containerization With Docker 

• Introduction to Containers
• Docker overview
• Docker commands
• Understanding Dockerfile
• Building Docker Containers
• Using Docker-Compose for building and testing software

AI Engineering & Vibe Coding

• Understanding AI in Software Development
  • AI’s role in modern programming workflows
  • Strengths and Limitations of AI-powered coding tools
• Exploring Free AI Coding Tools
  • GitHub Copilot: Auto completion & suggestions
  • Codeium: Full AI coding assistant
  • Tabnine: AI-assisted code predictions
  • Google Gemini for code: AI-generated coding assistance

Pydantic AI: Pythonic Agent Framework

• Schema-First Agents: Defining strict I/O using Pydantic Models to ensure JSON reliability
• Dependency Injection: Injecting database connections and API clients into agents securely

LangChain & LangGraph: Stateful Intelligence

• LangChain Essentials
  • Prompt templates
  • LCEL (LangChain Expression Language)
  • Tool-calling loops
• LangGraph Core Concepts
  • Nodes & Edges: Building state machines instead of linear chains
  • State Management: Using reducers to merge agent updates into a “source of truth”
  • Persistence & Checkpointing: Implementing SQLite/Postgres savers to pause and resume agent tasks
  • Human-in-loop: Designing nodes that wait for human approval before executing sensitive API calls
• LangSmith
  • Debugging complex traces
  • Running automate eval-frameworks

Model Context Protocol (MCP)

• The Protocol Spec
  • Understanding the Client-Server relationship in MCP
  • STDIO and Streamable HTTP
• Pre-built Servers
  • Connecting to GitHub, Google Drive and Postgres
• Building Custom MCP Servers
  • Creating Python/Node.js servers to expose internal legacy APIs to LLMs
  • Secure Resource Management: controlling file/data that the AI can “see”

Automation & Low Code Orchestration

• Multi-Agent n8n Workflows: building orchestrators that manage multiple sub-agents
• Self-Healing Workflows: Implementing error-handling loops that ask an LLM to “fix” a failed HTTP request
• Self-Hosting n8n: Deploying via Docker for enterprise data sovereignty

Enterprise RAG & Database Integration

• Advanced Vector Search: Hybrid search (Keyword + Semantic), reranking, and parent-document.
• Database bridges: Using SQL Agents to turn natural language into secure, read-only Postgres queries

Local AI, Privacy & Security

• Local LLM Deployment: Running Ollama, DeepSeek, and Qwen via Docker
• The AI Security Stack
  • Guardrails: Using Guardrails to prevent toxic or off-topic outputs
  • Compliance: Mapping agent behavior to GDPR and the EU AI Act
  • Prompt Injection Defense: Techniques for sanitizing user inputs in agentic workflows

Multi-Modal Agents & Deployment

• Voice Agents: Low-latency architecture for creating voice agents
• Generative Pipelines: Using tools to create visual assets
• Cloud hosting: Containerizing agents with Docker and deploying them to cloud

Capstone Project & Use Case

• Project Overview
• Complete projects to get experience and practice
• Presentation of project findings and Insights
• Industry Use Case Studies

Certification (Optional)

• Certification Overview
• Identify the right certification for you
• Tips to prepare for certification