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Obvious Introduction
A Kiki tree He has become a platform for beginners who are willing to learn new planning languages, ideas and skills. With growing interest in Agentic Ai, the platform increases the real projects focused on “agent travel,” making it a suitable meeting place.
One notable device Microsoft / Agents-Newsdenominating the 12 lessons' course that combines the basics of building agents AI. Each lesson is designed to stop itself, allowing you to start at any point that suits your needs. This structure provides many languages, to ensure comprehensive learning access. Each lesson in this course includes examples of the code, which can be found in the code_samples folder.
In addition, this course uses Azure AI was invented including GitHub modelatologists by contacting language models. It also includes fewer structures for a few Agent and services such as Autent Auture Auto app, Semantic kernelbeside Leopard.
To facilitate your decision-making process and provide a clear idea of what you will learn, we will update each study in detail. This guide works as a helping source for beginners who may feel uncertain about choosing the first place.
Obvious 1. Intro in AI agents and agent Use charges
The study introduces AI – Systems that are given to its natural models (lllms) are noticed in its environment, the reason for tools used, and many agent agents, and the agency, and many programs) for examples booking.
You will learn when you can enter agents to end agent, multi-steps, and advanced functions, and blocks of building a basis for Agentic solutions: Describe the tools, actions, and behavior.
Obvious 2. To test Ai Ageentic Frameworks
This course examines AI structures of ai medicines that have previously built-in and appears that you allow you to succeed, Tateth, and a quick Master by strengthening regular challenges and developing efficiency and developers.
It will Compare Microsoft AutoGen, Semantic Kernel, and the Service Managing Aise agent, and learn when your existing Azure Ecosystem is using representative.
Obvious 3. Understanding Ai Agentic Design patterns
This course introduces the Ai Aventic Damage Policies, Person-Centric Experience (UX) Construction Construction of Personal Personnel Experiences Between Ai Generative Ai.
You will learn the principles of use, and examples of their use, emphasis on the emphasis and completes the information caps, helping collaboration, and help people become a better partnering of their support.
Obvious 4. Tool Use pattern Design
The lesson introduces the design pattern, which allows the llM powered agents to control access to workouts such as work and APIs, enabling them to perform more than the text.
You will learn about charges for the key, including the restoration of the strong data, Code, functional variable, customer support, and content / edit. In addition, the lesson will cover the essential construction blocks of the design, such as well-defined tools, route and preferences, and error management (including recycling methods (including multiplication measures.
Obvious 5. Agentic Rag
The course describes Agentic Retrented Generage Generage Generage Generage Generage (RAG), how to return multiple-and-regring methods driven by large languages of languages (LLMS). In this way, the model comes into action, changes between tools / operating activities and systematic effects, evaluates results, repeated the process until a satisfactory response reaches. Usually using a maker-checker loop to improve accuracy and recover from incorrect examinations.
You will learn about situations where Agentic Rag Exlels, especially in accuracy – early conditions and the transgression of the API telephones. Additionally, you will find out how you take identity ownership process and use the useful loops that can improve reliability and results.
Obvious 6. Build Agents ai
This lesson teaches you how you can create honest AI emissions framework for a strong program (meta stimulators, and iterative analysis), to force the best security methods, and bring the feeling of quality user.
You will learn to identify and reduce risk, such as injection / purpose, unauthorized access, excessive load of service, information of information, and purple.
Obvious 7. Planning pattern editing
This course focuses on planning the construction of agents AI. Start by explaining a clear purpose and developing successful means. Then, break down with complex tasks to order and control subtasks.
Use organized releasing formats to ensure reliable, mechanical, mechanical answers, and use the inchestate of an event driven by the powerful functions and unexpected installation. Equiped agents with tools and guidelines about when and how to use them.
Continuously check the results of subtasks, measuring performance, and Tateth to enhance the final effects.
Obvious 8
The course describes a pattern of composing multiple agents, including the linking to many special agents to participate in a shared purpose. This method is especially effective in Complex, Cross-Domain, or approving activities benefits from division of staff and joint boundaries.
In this lesson, you will learn about the main building pattern of the design pattern: Orchestrator / Control, Communications, Communication Techniques, including conversation patterns, and a group.
Obvious 9. METICOTION DESIGN PATTERN
This lesson introduces satisfaction, which can be understandable as “thinks about thinking,” for Ai Agents. Meticotion allows these agents to recognize its consultation processes, explain their decisions, and adapt to the response to the response and past experiences.
You will learn the planning and evaluation techniques, such as showing, criticism, manufacturing patterns. These methods urge adjustments, helping to identify errors, and protect endless logs. In addition, these strategies will develop clearness, improve thinking quality, and support better conversion and see.
Obvious 10. AI agents in production
This lesson shows how you can change the “black box” to enter “glass box” using the “glass box” systems by using viewpoints and powerful testing techniques. You will be a model running like traces (representing the final-ending jobs) and complaints (applications that include language models or tools) using the same platforms LangFuse and Azure AI was established. This method will allow you to make the root error and analysis, to manage the latency and expenses, and trust assessments, safety, and acquisitions.
You will learn what features of the test, such as quality evacuation, safety, effective use of tool, and cost, and use performance development strategies and efficiency.
Obvious 11. Using Protocols in Agentic
This lesson introduces Agentic Protocols including ways in Agents connecting and participating. We will explore the three main protocols:
Protocol ModelConglect Protocol (MCP)Providing consistent access, customer access to tools, resources, and promoting, working as “universal adapter” in context and skills.
Agent-to-agent protocol (A2A)confirming secure, practical and practical communication between agents, complies with MCP.
Natural language Web language (NWEB)The enabling portion of the natural language of websites, allowing agents to find and share the web content.
In this lesson, you will learn about purpose and benefits of each protocol, how they give large-language models to communicate with the tools and other agents, and each one fits large buildings.
Obvious 12. Engineering Present Engineering Ai
This lesson introduces the target engineering, the target practice of providing for agents with relevant information, in the right, and timely format. This method makes them set up their following steps successfully, walking across the rapid writing of one time.
You will learn how different engineering is from the instant developer, because it includes continuous, powerful rather than static orders. Additionally, you will understand why the strategies such as writing, choosing, stress, stress, and divorce information is essential to be honest, especially the limitation of the compulsory window.
Obvious The last thoughts
This GitHub course It provides everything you need to start building agents AI. Including complete courses, short videos, and valid Python code. You can view articles on any command and use samples using GitTub models (available free) or Azure Ai Fedry.
Additionally, you will have a chance to work with Microsoft Agent Service, Semantic Kernel and Autogen. This course is a Source of public and open; Contributions are welcome, the issues are encouraged, and have been given a license from you to pass and expand.
Abid Awa (@ 1abidaswan) is a certified scientist for a scientist who likes the machine reading models. Currently, focus on the creation of the content and writing technical blogs in a machine learning and data scientific technology. Avid holds a Master degree in technical management and Bachelor degree in Telecommunication Engineering. His viewpoint builds AI product uses a Graph Neural network for students who strive to be ill.
