Master in Agentic AI: Building Autonomous Systems, Protocols & Workflows

Course Overview

Agentic AI is shifting technology from passive assistance to autonomous execution. This hands-on, project-focused course covers everything from foundational concepts to architecting multi-agent systems, protocol engineering (MCP/ACP), modular skills development, and deploying production-ready AI workflows that automate complex real-world software and business tasks.

Core Course Highlights

• Hands-on & Project-Driven: Build functional AI agents, plugins, and custom MCP servers from early modules.
• Modern Stack: Work with Python, PyTorch, TypeScript, LangChain/LangGraph, AutoGen, CrewAI, and n8n.
• Protocol & Extensibility Focused: Master Model Context Protocol (MCP), Agent Communication Protocol (ACP), custom skills, and plugin architectures.
• Production & MLOps: Deploy multi-agent systems with FastAPI/NestJS, Docker, observability tracing, and human-in-the-loop safeguards.

Complete Course Modules

Module 1: Foundations of Agentic AI & Extensibility

• Architecture of an AI Agent: Brain (LLM), Memory, Planning, Tools, and Extensions.
• Deterministic vs. Autonomous Execution.
• Prompt Engineering for Tool Calling, Structured Outputs (JSON/Pydantic), and Function Calling.
• Agent Extensibility Standards: Understanding protocols vs. hardcoded API integrations.

Module 2: Memory & Context Management

• Short-term vs. Long-term Memory in Agents.
• Vector Databases (ChromaDB, Qdrant, Pinecone) & Retrieval-Augmented Generation (RAG).
• Managing Large Context Windows (Gemini/Claude strategies), Summarization, and State Persistence.

Module 3: Developing Custom Skills, Plugins & Tools

• Connecting Agents to External APIs, Databases, and Web Browsers.
• Building Custom Skills & Modular Plugins: Creating decoupled, hot-swappable agent capabilities in Python and TypeScript/JavaScript.
• Plugin Architecture: Manifest files, schema definition, runtime skill injection, and dynamic capability loading.
• Error Handling, Tool Selection Logic, and Fallback Strategies.

Module 4: Protocol Engineering (MCP & ACP)

• Model Context Protocol (MCP): Developing custom MCP Servers and Clients to expose local databases, file systems, and tools securely to LLMs.
• Agent Communication Protocol (ACP): Standardizing inter-agent communication for message passing, task delegation, and state sharing across different runtime environments.
• Hands-on Lab: Converting legacy REST APIs into standard MCP resources and tools.

Module 5: Agent Frameworks & Orchestration

• LangGraph: Graph-based, stateful multi-agent workflows.
• CrewAI & AutoGen: Multi-agent collaboration, role specification, delegation, and ACP-driven inter-agent messaging.
• Low-Code Automation: Integrating agents and custom plugins into visual workflows using n8n.

Module 6: Autonomous Planning & Reasoning

• ReAct (Reasoning + Acting) Framework and Plan-and-Solve Strategies.
• Self-Reflection, Critique, and Iterative Self-Correction.
• Handling Non-Deterministic Outputs, Edge Cases, and Protocol Failures.

Module 7: Human-in-the-Loop & Safety Safeguards

• Designing Approval Steps for High-Risk Actions (e.g., write/delete operations via MCP).
• Security Best Practices: Prompt Injection Defense, Data Privacy, and Plugin Sandboxing.
• Rate Limiting, Cost Controls, and Token Monitoring.

Module 8: Production Deployment & MLOps

• Serving Agents, MCP Servers, and Custom Plugins via REST/gRPC APIs using FastAPI or NestJS.
• Containerization with Docker and Deployment to Cloud/Edge environments.
• Observability and Tracing using LangSmith, Phoenix, or Arize for tracking tool calls and protocol interactions.

Portfolio Projects

1. Custom MCP Server & Plugin SuiteDevelop a production-grade MCP server that securely exposes local databases, system telemetry, and custom developer tools directly to frontier LLMs.
2. Autonomous Web Researcher & WriterAn agent using dynamic skills and web-browsing plugins to synthesize complex information, verify sources, and output structured reports.
3. Multi-Agent Software Engineering Team (ACP-Enabled)A collaborative crew of agents (Product Manager, Coder, Reviewer, Tester) communicating via ACP to autonomously write, review, and test software modules.
4. Automated Business Workflow Pipeline (n8n + MCP Agents)An end-to-end workflow agent leveraging n8n, custom plugins, and MCP connections to monitor inbound communications, process data, update databases, and execute actions.

Course Takeaways & Student Outcomes

Upon completing the Master in Agentic AI course, students will graduate with the practical skills, system architecture knowledge, and hands-on experience required to build and deploy autonomous AI systems.

Here is what they will walk away with:

1. Hard Technical Skills & Framework Mastery

• Protocol & Extension Engineering: Ability to build and expose custom MCP (Model Context Protocol) servers and configure ACP (Agent Communication Protocol) for seamless inter-agent interaction.
• Modular Plugin & Skill Development: Expertise in creating decoupled, hot-swappable plugins and dynamic skills in Python and TypeScript/JavaScript.
• Multi-Agent Orchestration: Practical mastery of leading orchestration frameworks including LangGraph, CrewAI, AutoGen, and low-code platforms like n8n.
• Vector Databases & Memory Systems: Experience implementing short-term/long-term persistence, custom state management, and hybrid search using tools like ChromaDB, Qdrant, and Pinecone.

2. Enterprise System Architecture Capabilities

• Autonomous Reasoning & Self-Correction: Capability to design agents that use ReAct frameworks, plan-and-solve strategies, self-reflection, and automated loop debugging.
• Production Deployment & MLOps: Skills to containerize AI applications using Docker, serve agentic APIs via FastAPI/NestJS, and manage tracing/observability using LangSmith or Phoenix.
• Guardrails & Security: Understanding of human-in-the-loop workflows, prompt injection defenses, tool sandboxing, and token/cost optimization techniques.

3. Production-Ready Portfolio

Students will graduate with four fully built, functional portfolio projects ready to showcase to employers or clients:

1. Custom MCP Server & Plugin Suite: Exposing local databases and developer tooling directly to LLMs.
2. Autonomous Web Researcher & Writer: Multi-step browsing, synthesis, and structured report generation.
3. ACP-Enabled Software Engineering Crew: Collaborative multi-agent team (PM, Coder, Reviewer, Tester) executing automated dev cycles.
4. n8n + MCP Business Automation Pipeline: End-to-end automated workflow for processing inbound communications, database updates, and action execution.

4. High-Impact Career & Business Capabilities

• Transform from Developer to AI Architect: Shift from writing manual code or using standard LLM prompts to engineering autonomous, self-executing systems.
• Automate Complex Workflows: Ability to identify, design, and automate tedious enterprise processes across software engineering, customer support, and business operations.
• Future-Proof Expertise: Mastery over emerging, industry-standard protocols (MCP/ACP) that position students ahead of the curve in modern AI software development.