• WhatsApp: +1 571 4354 576
  • - Mail Us: eagamor@gmail.com
  • On All Socials @edukanea
Facebook-f X-twitter Instagram Youtube
login
Get Started free

AI Agents Learning Roadmap
edu Kanea > Courses > AI for Everyone > AI Agents Learning Roadmap

AI for Everyone Creative Tools

Last Update: Aug 08, 2025

AI Agents Learning Roadmap

  • Free
  • 60 Lesson

About Course

This course outlines a comprehensive 10-level progression for mastering AI agent development, from foundational concepts to advanced deployment and optimization. This course is designed to take learners from the basics of Generative AI and Transformers to building, optimizing, and deploying sophisticated AI agents in production environments. Each level (module) builds upon the previous one, covering key topics such as language modeling, retrieval-augmented generation, multi-agent systems, and advanced safety protocols.

What Will You Learn?

  • The "AI Agents Learning Roadmap" course teaches GenAI, Transformers, prompting, RAG, LLMOps, agent frameworks, memory, multi-agent collaboration, RL, safety protocols, and production deployment. You'll master building, optimizing, and deploying scalable agents, with practical case studies, preparing you for real-world AI challenges

Course Content

Foundations of GenAI and Transformers
This module lays the groundwork for understanding Generative AI (GenAI) and Transformer architectures, which are essential for building modern AI agents. Learners will explore the evolution from traditional machine learning (ML) to GenAI, dive into the mechanics of Transformers, and learn key training techniques such as pretraining and fine-tuning. The module concludes with a practical example to solidify conceptual understanding.

Language Model Behavior and Prompting
This module focuses on mastering the behavior of language models through effective prompting techniques, a critical skill for shaping AI agent outputs. Learners will explore the foundational mechanics of language models, learn to craft precise prompts, assign roles to models, and apply advanced prompting strategies. The module also addresses safety through guardrails and adversarial prompting, concluding with a practical case study to apply these concepts in real-world scenarios.

Retrieval-Augmented Generation (RAG)
This module explores Retrieval-Augmented Generation (RAG), a technique that combines retrieval of relevant external information with generative capabilities to enhance the performance of AI agents. Learners will understand the fundamentals of RAG, master embedding models and vector databases, design RAG pipelines, fine-tune systems for optimal performance, and apply RAG in practical scenarios, equipping them to build more accurate and context-aware agents.

LLMOps and Tool Integration
This module covers Large Language Model Operations (LLMOps) and the integration of tools to enhance the functionality of AI agents. Learners will gain a solid understanding of managing the lifecycle of large language models, connecting external tools and APIs, and leveraging frameworks like LangGraph and CrewAI. The module also explores advanced topics such as function calling, auto tool selection, and synthetic data generation, equipping learners to build robust, tool-augmented agents.

Agents and Agentic Frameworks
This module introduces the concept of AI agents, their necessity in modern systems, and the frameworks used to build them. Learners will explore what defines an agent, the various types and their applications, the mechanics of agent loops, and the process of constructing agents using popular frameworks. The module also covers evaluation loops for performance assessment and concludes with a practical case study, preparing learners to develop and deploy effective agent-based solutions as of 12:12 PM GMT on Tuesday, August 05, 2025.

Agent Memory, State & Orchestration
This module delves into the critical aspects of agent memory, state management, and orchestration, enabling learners to build sophisticated AI agents. It explores various memory types, strategies for managing context windows, the use of Redis for memory storage, function calling for dynamic interactions, symbolic memory for reasoning, and a practical orchestration example.

Multi-Agent Systems and Collaboration
This module explores multi-agent systems, focusing on collaboration protocols, team dynamics, and effective coordination among agents. Learners will understand the benefits and challenges of multi-agent collaboration, study architectural designs, implement collaboration protocols, address conflict resolution, evaluate agent performance through grading, and apply these concepts in a practical case study. This knowledge is crucial for building cooperative AI systems as of 11:58 AM GMT on August 06, 2025.

Evaluation, Feedback Loops, and RL
This module covers evaluation techniques, feedback loops, and reinforcement learning (RL) to optimize AI agent performance. Learners will explore building reward models, implementing advanced RL techniques like RLHF, RLAIF, and RLVR, grading reasoning chains, applying supervised fine-tuning, achieving iterative improvement, and applying these concepts in a practical example. These skills are essential for refining agents as of 01:32 PM GMT on August 06, 2025.

Protocols, Safety, and Advanced Alignment
This module focuses on safety protocols, alignment techniques, and advanced agent designs to ensure reliable and ethical AI behavior. Learners will explore the Action Selection Protocol (A2A), safety design principles, training and logging practices, guardrail implementation, self-verifying agents, and a practical case study. These skills are vital for developing secure and aligned agents as of 02:09 PM GMT on August 06, 2025.

Build, Optimize & Deploy in Production
This module teaches the process of building, optimizing, and deploying AI agents at scale in production environments. Learners will explore app frameworks for development, techniques for serving agents efficiently, cost optimization strategies, infrastructure considerations, observability for monitoring, and a practical deployment example. These skills are crucial for delivering robust, scalable, and cost-effective agent solutions as of 02:40 PM GMT on August 06, 2025.

Student Ratings & Reviews

No Review Yet
No Review Yet
  • Tag
Start Learning
  • All Levels
  • 0 Total Enrolled
  • 35 hours50 minutes Duration
  • Aug 8, 2025 Last Updated

A course by

EG

Audience

  • All Professions: Open to diverse backgrounds seeking AI skills.
  • AI Enthusiasts and Students: Seeking foundational to advanced AI knowledge.
  • Developers and Engineers: Building AI applications, especially agents.
  • Data Scientists: Interested in GenAI, RL, and deployment.
  • Tech Professionals: In industries like healthcare, finance, or e-commerce, aiming to integrate AI agents.
  • Researchers: Exploring multi-agent systems and safety protocols.
Emmanuel Gamor

Search here

Populer Course

Popular Category