Course Modules

A comprehensive curriculum to master agentic AI systems architecture

🧠 Learning Journey

Our curriculum is designed to take you from foundational concepts to advanced implementation techniques through a carefully structured learning path. Each module builds upon the previous ones, gradually developing your skills and knowledge in agentic AI systems architecture.

The course is designed for progressive learning, but you can also focus on specific modules based on your interests and prior knowledge. We recommend completing the modules in sequence for the most comprehensive understanding.

Each module includes theoretical content, practical examples, hands-on exercises, and assessment components to ensure a well-rounded learning experience.

Explore more AI learning paths at AIVibes

🎯 Course Completion

To complete the course, you should:

  • Study all module materials
  • Complete the practical exercises
  • Pass the module assessments
  • Implement the capstone project

Upon completion, you'll have the skills to design, implement, and deploy sophisticated agentic AI systems for real-world applications.

📚 Module Overview

Foundations of Agentic AI Systems

An introduction to the core concepts, historical evolution, and key capabilities of agentic AI systems.

Key Topics:
  • Definition and characteristics of agentic AI
  • Historical evolution of AI agents
  • Agent capabilities and limitations
  • Ethical considerations in agentic systems
  • Industry applications and case studies

Architectural Components

Exploring the fundamental building blocks that make up agentic AI systems architecture.

Key Topics:
  • Perception modules and input processing
  • Cognition and reasoning components
  • Action generation and execution
  • Memory systems and context management
  • Component integration patterns

Data Structures and Knowledge Representation

Understanding how to structure and represent knowledge for effective agent reasoning.

Key Topics:
  • Knowledge graphs and semantic networks
  • Vector embeddings and similarity spaces
  • Markov Decision Processes (MDPs)
  • Memory models for agent knowledge
  • Retrieval and reasoning mechanisms

Planning and Decision-Making Algorithms

Exploring algorithms that enable agents to plan actions and make decisions.

Key Topics:
  • Symbolic planning approaches
  • Reinforcement learning for decision-making
  • Search algorithms and heuristics
  • Handling uncertainty in planning
  • Hybrid planning approaches

Related: Decision Algorithms in AI Systems

Agent Development Frameworks and Tools

Learning to use modern frameworks and tools for building agentic AI systems.

Key Topics:
  • LangChain for agent development
  • AutoGen and multi-agent orchestration
  • LlamaIndex for knowledge integration
  • Prompt engineering techniques
  • Tool integration patterns

Multi-Agent Systems Design

Designing systems with multiple agents that collaborate to solve complex problems.

Key Topics:
  • Multi-agent architectures
  • Agent communication protocols
  • Role specialization and coordination
  • Conflict resolution mechanisms
  • Emergent behaviors in agent collectives

AIVibes Multi-Agent Solutions

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