Images by the author | Canva & Chatgpt
# introduction
github It has become the go-to platform for beginners looking to learn new programming languages, concepts and skills. With increasing interest in Agent AI, the platform is increasingly introducing real projects focused on “agent workflows,” making it an ideal environment for learning and building.
One notable resource is Microsoft/ai-agents-for-beginnersIt features 12 lesson courses that cover the basics of building AI agents. Each lesson is designed to stand out on its own and can start from any point that suits your needs. The repository also provides multilingual support, ensuring wide accessibility for learners. Each lesson in this course contains examples of code. code_samples folder.
Additionally, this course will be used Azure AI Foundry and GitHub Model Catalog To interact with language models. It also includes several AI agent frameworks and services Azure AI Agent Service, Semantic Kerneland Autogen.
Review each lesson in detail to facilitate the decision-making process and provide a clear overview of what you will learn. This guide serves as a useful resource for beginners who may find themselves uncertain about choosing a starting point.
# 1. Introducing AI Agents and Agent Use Cases
In this lesson, we will introduce you to AI agents. It is equipped with a large-scale environment-sensing language model (LLM) and explores the reasons for tools and knowledge, as well as examples of travelling behavioral agent types (simple/model-based reflection, goal/utility-based, learning, hierarchy, and multi-agent systems (MAS).
When to apply agents to open-ended, multi-step, improvised tasks, and the fundamental building blocks of agent solutions: definition of tools, actions, and behaviors.
# 2. Exploring the AI Agent Framework
This lesson explores AI agent frameworks with pre-built components and abstractions to help agents prototype, iterate and deploy faster by standardizing common challenges and improving scalability and developer efficiency.
Compare Microsoft Autogen, Semantic Kernel, and Managed Azure AI Agent services to learn how to use standalone tools and when to integrate with your existing Azure ecosystem.
# 3. Understanding AI Agent Design Patterns
This lesson introduces AI Agent Design Principles, a human-centered user experience (UX) approach to building customer-centered agent experiences within the inherent ambiguity of generated AI.
What principles are, practical guidelines for applying them, and examples of their use learn with an emphasis on agents who broaden and expand human capabilities, bridge knowledge gaps, promote collaboration, and help to become a better version of themselves through supportive and target-parallel interactions.
# 4. Use tool design patterns
In this lesson, we will introduce design patterns using tools. This allows agents with LLM to control access to external tools such as functions and APIs, allowing them to perform actions as well as generate text.
Learn about key use cases such as dynamic data retrieval, code execution, workflow automation, customer support integration, and content generation/editing. Additionally, this lesson covers the essential building blocks of this design pattern, including well-defined tool schemas, routing and selection logic, execution sandboxes, memory and observations, and error handling (including timeouts and retry mechanisms).
# 5. Agent Rug
This lesson explains multi-step search and seasonal approaches driven by large-scale language models (LLMs). Searched Generation of Agents (RAG). In this approach, the model plans actions, alternates between tool/function calls and structured output, evaluates results, refines the query, and repeats the process until you achieve a satisfactory answer. In many cases, manufacturer checker loops are used to improve accuracy and recover from incorrect queries.
Learn especially the situations that extend the exact initial scenario and tool integration workflows such as API calls. Additionally, it takes ownership of the inference process and discovers how using iterative loops can improve reliability and results.
# 6. Building a trustworthy AI agent
This lesson teaches you how to build a trusted AI agent by designing a robust system messaging framework (metaprompts, basic prompts, iterative improvements), implementing best security and privacy practices, and providing a quality user experience.
Learn to identify and mitigate risks, including prompts/goal injection, unauthorized system access, service overloads, knowledge-based addiction, and cascade errors.
# 7. Planning design patterns
This lesson focuses on planning and designing AI agents. Start by defining clear overall goals and establishing success criteria. Next, we break down complex tasks into ordered, manageable subtasks.
Use structured output formats to ensure reliable machine-readable responses and implement event-driven orchestration to address dynamic tasks and unexpected inputs. Equip your agents with the right tools and guidelines, and guidelines on when and how to use them.
Continuously evaluate subtask results, measure performance, and iterate to improve the final results.
# 8. Multi-agent design patterns
This lesson explains the design patterns for multi-agents. This involves coordinating multiple professional agents to work together towards shared goals. This approach is particularly effective for complex, domain, or parallelizable tasks that benefit from division of labor and coordinated handoffs.
In this lesson you will learn about the core building blocks of this design pattern, including orchestrator/controller, role definition agents, shared memory/state, communication protocols, and routing/handoff strategies that include sequential, simultaneous, and group chat patterns.
# 9. Metacognitive design patterns
In this lesson, we will introduce metacognition that AI agents can understand as “thinking about thinking.” MetAcognition allows these agents to monitor their own inference processes, explain decisions, and adapt based on feedback and past experience.
Learn planning and evaluation techniques such as reflection, critique, and maker checker patterns. These methods help to promote self-correction, identify errors, and prevent infinite inference loops. Furthermore, these techniques increase transparency, improve the quality of inference, and support better adaptation and recognition.
# 10. AI Agents in Roduction
This lesson shows how to convert a “black box” agent into a “glass box” system by implementing robust observability and evaluation techniques. You model as a trace (represents an end-to-end task) and use spans (petigations for specific steps that include language models or tools). langfuse and Azure Ai Foundry. This approach allows you to perform debugging and root cause analysis, manage delays and costs, and perform trust, safety and compliance audits.
Learn what aspects to assess, such as output quality, safety, tool call success, latency, cost, and other factors, and apply strategies to improve performance and effectiveness.
# 11. Using the Agent Protocol
This lesson introduces agent protocols that standardize how AI agents connect and collaborate. We consider three important protocols:
Model Context Protocol (MCP)provides consistent client-server access to tools, resources, and prompts that act as a “universal adapter” for context and functionality.
Agent to Agent Protocol (A2A)ensuring secure, interoperable communication and task delegation between agents, complementing MCP.
Natural Language Web Protocol (NLWEB)enabling a natural language interface for websites, allowing agents to discover and interact with web content.
In this lesson you will learn about the objectives and benefits of each protocol, how to enable large-scale language models (LLMs) to communicate with tools and other agents, and how each fits into a larger architecture.
# 12. Context Engineering for AI Agents
This lesson introduces context engineering. This is a disciplined practice of providing the right information to agents at the right information, the right format, and the right time. This approach allows you to effectively plan your next step and move beyond one-off prompt writing.
Learn how context engineering differs from rapid engineering because it involves ongoing dynamic curation rather than static instructions. Furthermore, given the limitations of constrained context windows, we can understand why strategies such as creating, selecting, compression, and separating information are essential for reliability.
# Final thoughts
this github course It provides everything you need to start building an AI agent. Includes comprehensive lessons, short videos and executable Python code. You can explore topics in any order and run the samples using GitHub models (free to use) or Azure AI Foundry.
Additionally, there is an opportunity to work with Microsoft's Azure AI Agent Services, Semantic Kernels, and Autogen. This course is community-driven open source. Contributions are welcome, issues are encouraged, and there is a license to fork and expand.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves building machine learning models. Currently he focuses on content creation and creates technical blogs on machine learning and data science technology. Abid holds a Masters degree in Technology Management and a Bachelor of Arts degree in Telecommunications Engineering. His vision is to build AI products using graph neural networks for students suffering from mental illness.
