What you'll learn:
- Learn Agentic AI Concepts: Transition from basic LLM prompting to designing autonomous agents capable of reasoning and planning.
- Build Multi-Agent Systems Hands-on: Learn to orchestrate workflows where multiple agents collaborate using frameworks like CrewAI and AWS Bedrock AgentCore.
- Implement Agentic Patterns: Gain experience with architectures including Retrieval-Augmented Generation - RAG, Model Context Protocol - MCP, and Agent Memory.
- Ensure Agent Security & Observability: Apply security best practices, and master Agent Observability.
- Validate AI Performance: Learn how to test and evaluate agent quality using data-driven metrics and the "LLM-as-a-Judge" framework.
- Develop Architectural Thinking: Acquire the "first principles" mindset needed to architect Agentic applications rather than just writing scripts.
- Learn inter-agent communication: Build bigger solutions using A2A.
Tired of AI projects that never reach production?
This course takes you from Agentic AI fundamentals to deploying production-ready agents using CrewAI and AWS.
Who This Is For: Engineers and developers who want to move beyond LLM prompting and build production AI agents with CrewAI, AWS Bedrock, and AWS AgentCore.
What You Will Build:
Multi-agent systems using CrewAI
Production Telegram Bot powered by AI agents (Capstone)
Retrieval-Augmented Generation (RAG) pipelines using AWS Bedrock Knowledge Base
Model Context Protocol (MCP) integrations using AWS AgentCore MCP Gateway
Observable agents on AWS with real-time monitoring
What You Will Understand:
Agentic fundamentals and multi-agent architectures
RAG to give agents access to your data using AWS Bedrock Knowledge Base
MCP for standardized tool access using AWS AgentCore MCP Gateway
Memory management for persistent agent context
Inter-agent communication (A2A) for collaborative agent systems
Agent security using AWS Bedrock Guardrails combined with security best practices
Observability using Langfuse and CloudWatch to monitor agent behavior in production
Agent evaluation using LLM-as-a-Judge methodology and other inline and online evaluation techniques
Why This Course:
Hands-on demos with real production patterns
Taught by a software architect with 20+ years of production experience
Full agent lifecycle: concept → build → secure → deploy → monitor
This is not another AI hype course. It is a practical blueprint for engineers building production AI agents with CrewAI and AWS.
Enroll now and start building today.
