ai-agents-for-beginners

Provides a 14-lesson curriculum organized into three complementary learning paths (Execution-Focused: Tool Use → Multi-Agent → Metacognition → Production; Data-Focused: Agentic RAG → Multi-Agent; Infrastructure-Focused: Frameworks → Protocols → Context Engineering → Memory) that converge on production deployment. Each lesson combines conceptual foundations with hands-on code samples in Python and .NET, enabling learners to choose entry points based on their primary concern (execution, data, or infrastructure) while ensuring all paths cover security, observability, and evaluation.

Teaches the Tool Use pattern through lessons that explain how agents invoke external functions via schema-based function calling, covering native bindings for OpenAI, Anthropic, and Ollama APIs. The curriculum demonstrates how agents parse LLM-generated function calls, validate arguments against schemas, execute tools, and feed results back into the agent loop, with code examples showing both synchronous and asynchronous tool invocation patterns.

Teaches building trustworthy agents through system message frameworks, value alignment, and safety guardrails. The curriculum covers how to design system prompts that encode agent values and constraints, how to implement content filtering and output validation, how to handle edge cases and adversarial inputs, and how to maintain transparency about agent capabilities and limitations. Code samples demonstrate safety patterns including input validation, output filtering, fact-checking, and escalation to humans for uncertain decisions.

Provides language-specific implementation guides for Python and .NET showing how to implement agent patterns using each language's idioms, libraries, and frameworks. The curriculum includes setup instructions, dependency management, async/await patterns, and framework-specific examples for AutoGen, Semantic Kernel, and other tools. Code samples demonstrate how to handle language-specific challenges (async in Python vs. C#, type safety, dependency injection) and how to integrate with language-specific ecosystems.

Teaches agentic protocols as standardized communication mechanisms enabling agents built with different frameworks to interoperate. The curriculum covers Model Context Protocol (MCP) as a standard for agent-to-agent and agent-to-tool communication, including protocol specifications, implementation patterns, and integration with existing frameworks. Code samples demonstrate how to implement MCP servers and clients, how to expose tools via MCP, and how to build agent networks using standardized protocols.

Teaches workflow orchestration patterns for deploying and managing agents in production, including CI/CD pipelines, automated testing, and deployment strategies. The curriculum covers how to structure agent code for testability, how to implement integration tests for agent behavior, how to automate deployment to cloud platforms, and how to manage agent versions and rollbacks. Code samples demonstrate GitHub Actions workflows, Azure Pipelines, and container-based deployment patterns.

Teaches Agentic RAG (Retrieval-Augmented Generation) as a pattern where agents decide when to retrieve external knowledge, what queries to formulate, and how to integrate retrieved context into reasoning. The curriculum covers context types (conversation history, retrieved documents, system prompts, scratchpads), context window management, and techniques like chat summarization to keep context within token limits while preserving semantic meaning. Code samples demonstrate how agents use retrieval as a tool within the agent loop.

Teaches multi-agent patterns where multiple specialized agents collaborate to solve complex problems through defined communication protocols. The curriculum covers agent-to-agent (A2A) protocols and Model Context Protocol (MCP) for standardized agent communication, demonstrating how agents can delegate subtasks, aggregate results, and coordinate execution. Code samples show both sequential and parallel multi-agent workflows with explicit handoff mechanisms and result aggregation strategies.

Teaches planning patterns where agents break down complex goals into actionable steps using reasoning chains (chain-of-thought, tree-of-thought). The curriculum demonstrates how agents generate plans, validate feasibility, adapt when steps fail, and track progress. Code samples show how to structure agent prompts to encourage explicit reasoning, how to parse and execute generated plans, and how to handle replanning when conditions change.

Teaches metacognition as a pattern where agents reflect on their own reasoning, evaluate the quality of their outputs, and iteratively improve. The curriculum demonstrates how agents can use self-critique, ask clarifying questions, verify facts, and decide when to seek human feedback. Code samples show how to structure prompts for self-reflection, how to implement feedback loops, and how to track improvement over multiple iterations.

Provides structured comparison of three major agent frameworks (AutoGen, Semantic Kernel, Azure AI Agent Service, Microsoft Agent Framework) covering their architectural differences, strengths, limitations, and appropriate use cases. The curriculum includes code samples in each framework showing how to implement the same agent pattern, enabling developers to evaluate frameworks based on their specific requirements (multi-agent support, language support, deployment model, cost structure).

Teaches production deployment patterns including observability (logging, tracing, monitoring), evaluation frameworks for assessing agent quality, and operational considerations (scaling, cost management, security). The curriculum covers how to instrument agents for production monitoring, how to define and measure success metrics, how to implement guardrails and safety checks, and how to handle failures gracefully. Code samples demonstrate integration with Azure Monitor, application insights, and custom evaluation pipelines.

Teaches context engineering as the practice of structuring and optimizing the information provided to agents to improve reasoning quality. The curriculum covers context types (conversation history, retrieved documents, system prompts, scratchpads), techniques for managing context within token limits (summarization, prioritization, chunking), and prompt optimization strategies. Code samples demonstrate how to structure system prompts for different agent roles, how to format retrieved context for maximum relevance, and how to use scratchpads for intermediate reasoning.

Teaches agent memory patterns including short-term memory (conversation context), long-term memory (persistent knowledge bases), and episodic memory (past interactions). The curriculum covers how agents retrieve relevant memories, update memory based on new information, and manage memory consistency. Code samples demonstrate integration with vector databases for semantic memory retrieval, relational databases for structured facts, and techniques for memory consolidation and forgetting.