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# Introduction
GitHub has grow to be the go-to platform for inexperienced persons wanting to study new programming languages, ideas, and expertise. With the rising curiosity in agentic AI, the platform is more and more showcasing actual tasks that target “agentic workflows,” making it a really perfect setting to study and construct.
One notable useful resource is microsoft/ai-agents-for-beginners, which encompasses a 12-lesson course protecting the basics of constructing AI brokers. Every lesson is designed to face by itself, permitting you to start out at any level that fits your wants. This repository additionally presents multi-language help, guaranteeing broader accessibility for learners. Every lesson on this course contains code examples, which may be discovered within the code_samples folder.
Furthermore, this course makes use of Azure AI Foundry and GitHub Mannequin Catalogs for interacting with language fashions. It additionally incorporates a number of AI agent frameworks and companies like Azure AI Agent Service, Semantic Kernel, and AutoGen.
To facilitate your decision-making course of and supply a transparent overview of what you’ll study, we’ll assessment every lesson intimately. This information serves as a useful useful resource for inexperienced persons who may really feel unsure about selecting a place to begin.
# 1. Intro to AI Brokers and Agent Use Circumstances
This lesson introduces AI brokers — methods powered by giant language fashions (LLMs) that sense their setting, cause over instruments and information, and act — and surveys key agent sorts (easy/model-based reflex, purpose/utility-based, studying, hierarchical, and multi-agent methods (MAS)) by means of travel-booking examples.
You’ll study when to use brokers to open-ended, multi-step, and improvable duties, and the foundational constructing blocks of agentic options: defining instruments, actions, and behaviors.
# 2. Exploring AI Agentic Frameworks
This lesson explores AI agent frameworks with pre-built parts and abstractions that allow you to prototype, iterate, and deploy brokers quicker by standardizing frequent challenges and boosting scalability and developer effectivity.
You’ll evaluate Microsoft AutoGen, Semantic Kernel, and the managed Azure AI Agent Service, and study when to combine together with your current Azure ecosystem versus utilizing standalone instruments.
# 3. Understanding AI Agentic Design Patterns
This lesson introduces AI agentic design ideas, a human-centric consumer expertise (UX) strategy for constructing customer-focused agent experiences amid the inherent ambiguity of generative AI.
You’ll study what the ideas are, sensible tips for making use of them, and examples of their use, with an emphasis on brokers that broaden and scale human capacities, fill information gaps, facilitate collaboration, and assist individuals grow to be higher variations of themselves by means of supportive, goal-aligned interactions.
# 4. Device Use Design Sample
This lesson introduces the tool-use design sample, which permits LLM-powered brokers to have managed entry to exterior instruments similar to features and APIs, enabling them to take actions past simply producing textual content.
You’ll find out about key use circumstances, together with dynamic knowledge retrieval, code execution, workflow automation, buyer help integrations, and content material technology/modifying. Moreover, the lesson will cowl the important constructing blocks of this design sample, similar to well-defined instrument schemas, routing and choice logic, execution sandboxing, reminiscence and observations, and error dealing with (together with timeout and retry mechanisms).
# 5. Agentic RAG
This lesson explains agentic retrieval-augmented technology (RAG), a multi-step retrieval-and-reasoning strategy pushed by giant language fashions (LLMs). On this strategy, the mannequin plans actions, alternates between instrument/operate calls and structured outputs, evaluates outcomes, refines queries, and repeats the method till attaining a passable reply. It usually makes use of a maker-checker loop to reinforce correctness and recuperate from malformed queries.
You’ll study concerning the conditions the place agentic RAG excels, notably in correctness-first situations and prolonged tool-integrated workflows, similar to API calls. Moreover, you’ll uncover how taking possession of the reasoning course of and utilizing iterative loops can improve reliability and outcomes.
# 6. Constructing Reliable AI Brokers
This lesson teaches you tips on how to construct reliable AI brokers by designing a sturdy system message framework (meta prompts, fundamental prompts, and iterative refinement), implementing safety and privateness greatest practices, and delivering a top quality consumer expertise.
You’ll study to determine and mitigate dangers, similar to immediate/purpose injection, unauthorized system entry, service overloading, knowledge-base poisoning, and cascading errors.
# 7. Planning Design Sample
This lesson focuses on planning design for AI brokers. Begin by defining a transparent general purpose and establishing success standards. Then, break down complicated duties into ordered and manageable subtasks.
Use structured output codecs to make sure dependable, machine-readable responses, and implement event-driven orchestration to handle dynamic duties and surprising inputs. Equip brokers with the suitable instruments and tips for when and tips on how to use them.
Constantly consider the outcomes of the subtasks, measure efficiency, and iterate to enhance the ultimate outcomes.
# 8. Multi-Agent Design Sample
This lesson explains the multi-agent design sample, which entails coordinating a number of specialised brokers to collaborate towards a shared purpose. This strategy is especially efficient for complicated, cross-domain, or parallelizable duties that profit from the division of labor and coordinated handoffs.
On this lesson, you’ll study concerning the core constructing blocks of this design sample: an orchestrator/controller, role-defined brokers, shared reminiscence/state, communication protocols, and routing/hand-off methods, together with sequential, concurrent, and group chat patterns.
# 9. Metacognition Design Sample
This lesson introduces metacognition, which may be understood as “occupied with pondering,” for AI brokers. Metacognition permits these brokers to observe their very own reasoning processes, clarify their choices, and adapt primarily based on suggestions and previous experiences.
You’ll study planning and analysis strategies, similar to reflection, critique, and maker-checker patterns. These strategies promote self-correction, assist determine errors, and stop countless reasoning loops. Moreover, these strategies will improve transparency, enhance the standard of reasoning, and help higher adaptation and notion.
# 10. AI Brokers in Manufacturing
This lesson demonstrates tips on how to remodel “black field” brokers into “glass field” methods by implementing strong observability and analysis strategies. You’ll mannequin runs as traces (representing end-to-end duties) and spans (petitions for particular steps involving language fashions or instruments) utilizing platforms like Langfuse and Azure AI Foundry. This strategy will allow you to carry out debugging and root-cause evaluation, handle latency and prices, and conduct belief, security, and compliance audits.
You’ll study what elements to guage, similar to output high quality, security, tool-call success, latency, and prices, and apply methods to reinforce efficiency and effectiveness.
# 11. Utilizing Agentic Protocols
This lesson introduces agentic protocols that standardize the methods AI brokers join and collaborate. We are going to discover three key protocols:
Mannequin Context Protocol (MCP), which gives constant, client-server entry to instruments, sources, and prompts, functioning as a “common adapter” for context and capabilities.
Agent-to-Agent Protocol (A2A), which ensures safe, interoperable communication and activity delegation between brokers, complementing the MCP.
Pure Language Net Protocol (NLWeb), which permits natural-language interfaces for web sites, permitting brokers to find and work together with internet content material.
On this lesson, you’ll study concerning the objective and advantages of every protocol, how they allow giant language fashions (LLMs) to speak with instruments and different brokers, and the place every matches into bigger architectures.
# 12. Context Engineering for AI Brokers
This lesson introduces context engineering, which is the disciplined observe of offering brokers with the precise info, in the precise format, and on the proper time. This strategy permits them to plan their subsequent steps successfully, transferring past one-time immediate writing.
You’ll find out how context engineering differs from immediate engineering, because it entails ongoing, dynamic curation relatively than static directions. Moreover, you’ll perceive why methods similar to writing, choosing, compressing, and isolating info are important for reliability, particularly given the constraints of constrained context home windows.
# Ultimate Ideas
This GitHub course gives all the pieces you’ll want to begin constructing AI brokers. It contains complete classes, brief movies, and runnable Python code. You possibly can discover matters in any order and run samples utilizing GitHub Fashions (accessible free of charge) or Azure AI Foundry.
Moreover, you should have the chance to work with Microsoft’s Azure AI Agent Service, Semantic Kernel, and AutoGen. This course is community-driven and open supply; contributions are welcome, points are inspired, and it’s licensed so that you can fork and prolong.
Abid Ali Awan (@1abidaliawan) is an authorized knowledge scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students battling psychological sickness.
