Photo by the author Canva & Chatgpt
# Entry
Girub It has become a platform for beginners who are eager to learn fresh programming, concepts and skills. Along with the growing interest in Agentic AI, the Platform is increasingly presenting real projects that focus on “agency work flows”, which makes it an ideal environment for learning and building.
One significant resource is Microsoft/AI-AGENTS-FOR-BEGINNERSwhich contains a 12-person course covering the basics of building AI agents. Each lesson is designed to stand alone, which allows you to start at any time that meets your needs. This repository also offers support in multilingual language, providing wider accessibility to students. Each lesson at this course contains examples of code that can be found in code_samples folder.
In addition, this course uses Azure ai foundry AND GitHub model catalogs for interaction with language models. It also contains several frames and services of AI agents, such as Azure AI Agent ServiceIN Semantic nucleusAND Autogenous.
To facilitate the decision -making process and present a clear review of what you learn, we review each lesson in detail. This guide serves as a helpful resource for beginners who can feel certain about choosing a starting point.
# 1. Introduction to AI agents and cases of using an agent
This lesson presents AI-Systems powered by immense models (LLM), which sense their environment, reason for tools and knowledge, and operate-and study agents (straightforward/reflex based on models, based on goals/tools, learning, hierarchical and multi-magent (mass)) using examples of books.
You will learn when to operate agents for open, multi -stage and improvement and fundamental building blocks of building agency solutions: defining tools, activities and behaviors.
# 2. Explore the AI agency framework
This lesson explores the framework of AI agents with pre -built components and abstractions that allow you to prototype, iterate and implement agents faster by standardizing common challenges and increasing the scalability of programmers.
You compare Microsoft AutoGen, a semantic nucleus and agent Azure AI service and you will learn when it integrates with the existing Azure ecosystem compared to using independent tools.
# 3
This lesson introduces AI AI design principles, an approach -oriented approach to the user’s experience (UX) to build the experience of customer -oriented agents among the inseparable ambiguous ambiguity of artificial intelligence.
You will learn what the rules are, practical guidelines for their operate, and examples of their operate, with an emphasis on agents expanding and scaling human abilities, fill in knowledge gaps, facilitate cooperation and aid people become better versions of themselves through supporting interactions adapted for purposes.
# 4. Tool Apply the design pattern
This lesson introduces a model for the operate of tools, which allows LLM powered agents to control access to external tools, such as API functions and interfaces, enabling them to take action outside the text generating.
You will learn about key operate cases, including active data search, code making, automation of work flow, integration of customer service and generating/editing content. In addition, the lesson will include the necessary structural elements of this design pattern, such as well -defined tool diagrams, routing and selection logic, sandbox, memory and observations as well as error service (including time and retra limit mechanisms).
# 5. Agentic Rag
This lesson explains the generation of Agent Contentvaluused Generation (RAG), a multi -stage approach to download and justification driven by immense language models (LLM). In this approach, the model plans actions, change between the calls of tools/functions and structural outputs, evaluates the results, improved inquiries and repeats the process until a satisfactory answer is achieved. He often uses a loop of producers to enhance correctness and recover after distorted queries.
You will learn about situations in which Agentic RAG stands out, especially in the scenarios of the original correctness and extended work flows integrated with tools, such as API calls. In addition, you will discover how the acquisition of ownership of the reasoning process and the operate of iteration loops can enhance reliability and results.
# 6. Building trustworthy AI agents
This lesson teaches how to build reliable agents of artificial intelligence, designing a solid framework for system messages (meta, basic hints and iterative improvement), enforcement of the best security and privacy practices and ensuring a high quality user experience.
You will learn to identify and alleviate the risk such as speedy/injection to the target, unauthorized system access, overloading services, knowledge poisoning and cascade errors.
# 7. Design pattern planning
This lesson focuses on planning the design of AI agents. Start by defining a clear goal and establishing success criteria. Then spread the complicated tasks into ordered subtask.
Apply structured output formats to ensure reliable, machine answers and implement an orchestration based on events to solve active tasks and unexpected entrances. Equip agents with appropriate tools and guidelines when and how to operate them.
Still assess the results of the sub -pit, measure efficiency and ite to improve the final results.
# 8. The design of the project of many agents
This lesson explains the multi -stage design pattern, which includes the coordination of many specialized agents to cooperate in the direction of a common goal. This approach is particularly effective in the case of complicated, between domains or parallel tasks that operate the division of the workforce and coordinated messages.
In this lesson you will learn about the basic structural elements of this project pattern: orchestrator/controller, agents defined by the role, joint memory/state, communication protocols and routing/transmission strategies, including sequential, co -creating and group chat patterns.
# 9. Model design of the Metap Disty project
This lesson introduces a metapognition that can be understood as “thinking about thinking” for AI agents. Metacognition allows these agents to monitor their own reasoning processes, explain their decisions and adapt on the basis of feedback and past experience.
You will learn planning and evaluation techniques such as reflection, criticism and manufacturer. These methods promote independent corrections, aid identify errors and prevent the endless reasoning of the loop. In addition, these techniques will enhance transparency, improve the quality of reasoning and support better adaptation and perception.
# 10. AI agents in production
This lesson shows how to transform the “black box” agents into “glass” systems, implementing solid observation and evaluation techniques. You model the course as traces (representing comprehensive tasks) and spreading (petitions regarding specific steps covering models or language tools) using platforms such as platforms Lancer and Azure AI foundry. This approach will allow you to conduct an analysis of debugging and causes of roots, manage delays and costs, and conduct audits of trust, security and compliance.
You will learn what aspects should be assessed, such as output quality, safety, the success of convening a tool, delay and costs, and apply strategies to enhance efficiency and effectiveness.
# 11. Using agency protocols
This lesson introduces agency protocols that standardize how AI agents connect and cooperate. We will examine three key reports:
Context Protocol (MCP)which provides a coherent, customer-server access to tools, resources and hints, functioning as a “universal adapter” for context and possibilities.
Agent-agent protocol (A2A)which provides a sheltered, interoperable delegation of communication and tasks between agents, supplementing MCP.
Natural language internet protocol (NLWEB)which enables natural language interfaces for websites, enabling agents to discover and interact with the content of the network.
In this lesson, you will learn about the purpose and benefits of each protocol, how they allow immense language models (LLM) to communicate with tools and other agents, and where everyone fits into larger architecture.
# 12. Contextral engineering for AI agents
This lesson introduces contextual engineering, which is a disciplined practice of providing agents with the right information, in the right format and in the right time. This approach allows them to effectively plan the next steps, going beyond one -time quick writing.
You will learn how contextual engineering differs from speedy engineering, because it includes a ongoing, active treatment, not immobile instructions. In addition, you will understand why strategies such as writing, selection, compression and isolation of information are necessary for reliability, especially considering the limitations of narrow contextual windows.
# Final thoughts
This GitHub course It provides everything you need to start building AI agents. Contains comprehensive lessons, low films and Runnable Python Code. You can discover topics in any order and run the samples using GitHub models (available for free) or Azure AI foundry.
In addition, you will have the opportunity to cooperate with Azure AI Agent Service Microsoft, the semantic nucleus and autogen. This course is based on community and open source; The cartridges are welcome, problems are encouraged and this is licensed so that you can fork and extend.
Abid Ali Awan (@1abidaliawan) is a certified scientist who loves to build machine learning models. Currently, it focuses on creating content and writing technical blogs on machine learning and data learning technologies. ABID has a master’s degree in technology management and a bachelor’s title in the field of telecommunications engineering. His vision is to build AI with a neural network for students struggling with mental illness.