{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-microsoft--ai-agents-for-beginners","slug":"microsoft--ai-agents-for-beginners","name":"ai-agents-for-beginners","type":"agent","url":"https://aka.ms/ai-agents-beginners","page_url":"https://unfragile.ai/microsoft--ai-agents-for-beginners","categories":["automation"],"tags":["agentic-ai","agentic-framework","agentic-rag","ai-agents","ai-agents-framework","autogen","generative-ai","semantic-kernel"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"github-microsoft--ai-agents-for-beginners__cap_0","uri":"capability://planning.reasoning.structured.agent.curriculum.with.multiple.learning.paths","name":"structured-agent-curriculum-with-multiple-learning-paths","description":"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.","intents":["I want to learn AI agents from the ground up but don't know where to start","I need to understand which framework (AutoGen, Semantic Kernel, Azure AI Agent Service) fits my use case","I want to build agents for production but need guidance on security and observability","I'm a .NET developer and need agent patterns in my ecosystem, not just Python"],"best_for":["software developers with Python or .NET experience learning agentic patterns","technical architects evaluating agent frameworks for production systems","teams transitioning from traditional software to LLM-based agent architectures"],"limitations":["Curriculum is educational, not a production framework itself — requires implementing patterns using external frameworks","Jupyter Notebook format limits IDE integration and testing workflows compared to native project templates","No built-in hands-on sandbox environment — learners must set up local development or cloud environments"],"requires":["Python 3.9+ or .NET 6+ depending on chosen implementation path","Basic programming knowledge in Python or C#","API keys for OpenAI, Anthropic, or Azure AI services for framework examples","Git and GitHub access to clone the repository"],"input_types":["markdown lesson content","jupyter notebooks with executable code","code samples in Python and C#"],"output_types":["working agent implementations","conceptual understanding of agentic patterns","production deployment checklists"],"categories":["planning-reasoning","educational-curriculum"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_1","uri":"capability://tool.use.integration.tool.use.pattern.teaching.with.schema.based.function.calling","name":"tool-use-pattern-teaching-with-schema-based-function-calling","description":"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.","intents":["I want to understand how agents decide which tools to call and when","I need to implement function calling that works across multiple LLM providers","I want to see real examples of agents calling APIs, databases, or custom functions","I need to validate tool arguments and handle execution errors gracefully"],"best_for":["developers building agents that need to interact with external systems","teams implementing multi-provider LLM strategies where tool calling syntax differs","builders creating custom tools and needing to expose them to agents safely"],"limitations":["Curriculum teaches patterns but doesn't provide a unified tool registry abstraction — developers must implement provider-specific logic","No built-in tool validation framework — error handling and schema enforcement must be implemented per use case","Async tool execution patterns are covered but orchestration of parallel tool calls requires external frameworks"],"requires":["Understanding of function signatures and JSON schema","API keys for at least one LLM provider (OpenAI, Anthropic, Ollama)","Familiarity with async/await patterns in Python or C#"],"input_types":["LLM-generated function call specifications","JSON schemas defining tool parameters","tool execution results (text, structured data, errors)"],"output_types":["validated function calls ready for execution","tool execution results formatted for LLM consumption","error handling strategies and fallback patterns"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_10","uri":"capability://safety.moderation.trustworthiness.and.safety.framework.for.agent.alignment","name":"trustworthiness-and-safety-framework-for-agent-alignment","description":"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.","intents":["I want to ensure my agent behaves according to organizational values and policies","I need to prevent agents from generating harmful, biased, or misleading content","I want agents to acknowledge uncertainty and escalate decisions when appropriate","I need to maintain transparency about what agents can and cannot do"],"best_for":["teams building agents for regulated industries (healthcare, finance, legal) with strict compliance requirements","organizations deploying agents to external users where safety and alignment are critical","developers implementing agents where transparency and explainability are important"],"limitations":["System message frameworks can be circumvented through prompt injection — requires additional input validation","Content filtering and output validation add latency and may reject valid outputs","Curriculum teaches patterns but doesn't provide automated safety verification — requires custom evaluation and testing"],"requires":["Understanding of prompt injection attacks and defenses","Familiarity with content filtering and safety evaluation techniques","Knowledge of bias detection and mitigation strategies"],"input_types":["system message specifications and value definitions","safety policies and constraints","user inputs and agent outputs","feedback on safety violations"],"output_types":["aligned system prompts","filtered and validated outputs","safety evaluation reports","escalation decisions and human review requests"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_11","uri":"capability://code.generation.editing.python.and.dotnet.implementation.guides.with.framework.specific.patterns","name":"python-and-dotnet-implementation-guides-with-framework-specific-patterns","description":"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.","intents":["I'm a Python developer and want to build agents using Python idioms and libraries","I'm a .NET developer and need agent patterns in C# with async/await","I want to understand how to set up development environments for agent development","I need to integrate agents with existing Python or .NET applications"],"best_for":["Python developers building agents with libraries like AutoGen, LangChain, or Semantic Kernel",".NET developers implementing agents in C# with Semantic Kernel or custom frameworks","teams with existing Python or .NET codebases wanting to add agent capabilities"],"limitations":["Language-specific guides may lag behind framework updates — requires verification against current framework documentation","Async patterns differ significantly between Python and .NET — code examples may not be directly portable","Curriculum covers basic patterns but doesn't address advanced language-specific optimizations (e.g., C# source generators, Python async context managers)"],"requires":["Python 3.9+ or .NET 6+ depending on chosen language","Familiarity with async/await patterns in chosen language","Package management tools (pip for Python, NuGet for .NET)"],"input_types":["language-specific code samples","framework documentation","dependency specifications"],"output_types":["working agent implementations in Python or .NET","setup and configuration guides","best practices for language-specific development"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_12","uri":"capability://tool.use.integration.agentic.protocols.and.interoperability.standards.including.model.context.protocol","name":"agentic-protocols-and-interoperability-standards-including-model-context-protocol","description":"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.","intents":["I want agents built with different frameworks to communicate with each other","I need to expose my tools and services to agents via a standard protocol","I want to build agent networks where agents can discover and call each other","I need to ensure my agents can interoperate with third-party agents and services"],"best_for":["organizations building agent ecosystems with multiple frameworks and tools","teams implementing agent networks where interoperability is critical","developers creating tools and services that need to be accessible to agents"],"limitations":["MCP is a relatively new standard — adoption and tooling are still evolving","Protocol overhead adds latency compared to direct framework-specific communication","Curriculum teaches patterns but doesn't provide MCP implementation libraries — requires custom implementation or framework support"],"requires":["Understanding of protocol design and message passing","Familiarity with JSON-RPC or similar RPC mechanisms","Knowledge of agent capabilities and tool definitions"],"input_types":["protocol specifications and standards","tool and capability definitions","inter-agent messages and requests"],"output_types":["MCP server and client implementations","protocol-compliant tool definitions","agent network topologies and communication patterns"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_13","uri":"capability://automation.workflow.workflow.orchestration.and.ci.cd.patterns.for.agent.deployment","name":"workflow-orchestration-and-ci-cd-patterns-for-agent-deployment","description":"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.","intents":["I want to automate testing and deployment of my agents","I need to implement CI/CD pipelines for agent code","I want to manage agent versions and enable rollbacks if needed","I need to deploy agents to cloud platforms with proper configuration management"],"best_for":["teams implementing DevOps practices for agent systems","organizations deploying agents to production with continuous integration","developers managing multiple agent versions and environments"],"limitations":["Agent testing is challenging because outputs depend on LLM behavior — deterministic testing is difficult","Curriculum teaches patterns but doesn't provide pre-built CI/CD templates — requires customization for specific platforms","Deployment strategies vary significantly by cloud platform — examples may not be directly portable"],"requires":["Understanding of CI/CD concepts and tools (GitHub Actions, Azure Pipelines, etc.)","Familiarity with containerization (Docker) and orchestration (Kubernetes)","Knowledge of testing strategies for non-deterministic systems"],"input_types":["agent code and configuration","test specifications and evaluation criteria","deployment target specifications"],"output_types":["CI/CD pipeline definitions","test results and quality metrics","deployed agent instances and versions"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_2","uri":"capability://memory.knowledge.agentic.rag.pattern.with.context.engineering","name":"agentic-rag-pattern-with-context-engineering","description":"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.","intents":["I want agents to answer questions using my proprietary documents or knowledge bases","I need agents to decide when retrieval is necessary vs. when to use existing knowledge","I want to manage token budgets while keeping rich context for reasoning","I need to understand how to structure context so agents can reason effectively"],"best_for":["teams building knowledge-intensive agents (customer support, documentation Q&A, research assistants)","organizations with large document collections needing intelligent retrieval","developers optimizing token usage in long-running agent conversations"],"limitations":["Curriculum teaches patterns but doesn't provide a vector database or retrieval engine — requires integration with external services (Pinecone, Weaviate, Azure Cognitive Search)","Context summarization techniques are covered conceptually but implementation quality depends on the summarization model used","No built-in evaluation metrics for retrieval quality — developers must implement custom evaluation"],"requires":["Understanding of vector embeddings and semantic search","Access to a vector database or retrieval service","Document collection or knowledge base to index","Familiarity with prompt engineering for retrieval queries"],"input_types":["user queries","document collections","conversation history","system prompts and context specifications"],"output_types":["retrieval queries formulated by agents","retrieved document chunks with relevance scores","context-augmented agent responses","summarized conversation history"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_3","uri":"capability://planning.reasoning.multi.agent.orchestration.patterns.with.communication.protocols","name":"multi-agent-orchestration-patterns-with-communication-protocols","description":"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.","intents":["I want to decompose complex problems into subtasks handled by specialized agents","I need agents to communicate with each other using standard protocols","I want to understand when to use sequential vs. parallel multi-agent execution","I need to aggregate and reconcile results from multiple agents"],"best_for":["teams building complex reasoning systems that benefit from task specialization","organizations adopting Model Context Protocol for agent interoperability","developers creating agent networks where agents need to discover and communicate with each other"],"limitations":["Multi-agent coordination adds latency — sequential handoffs introduce ~200-500ms per agent transition","No built-in consensus mechanism for conflicting agent outputs — requires custom reconciliation logic","Curriculum covers patterns but doesn't provide a multi-agent orchestration framework — requires external tools (AutoGen, Semantic Kernel, or custom implementation)"],"requires":["Understanding of agent design and tool use patterns","Familiarity with async/await for parallel agent execution","Knowledge of communication protocols (REST, message queues, or MCP)","Design patterns for task decomposition and result aggregation"],"input_types":["complex user queries","task decomposition specifications","agent capability definitions","inter-agent messages and results"],"output_types":["task decomposition plans","agent-to-agent communication messages","aggregated results from multiple agents","execution traces showing agent interactions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_4","uri":"capability://planning.reasoning.planning.and.task.decomposition.with.reasoning.chains","name":"planning-and-task-decomposition-with-reasoning-chains","description":"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.","intents":["I want agents to think through problems step-by-step before acting","I need agents to generate and execute multi-step plans for complex tasks","I want agents to adapt their plans when steps fail or conditions change","I need to understand how to structure prompts to encourage better reasoning"],"best_for":["developers building agents for complex problem-solving (research, planning, troubleshooting)","teams implementing agents that need explainable decision-making","builders creating agents where reasoning transparency is important for debugging"],"limitations":["Explicit reasoning chains increase token consumption and latency — chain-of-thought can add 30-50% to response time","Plan quality depends heavily on prompt engineering — no automatic optimization of planning strategies","Curriculum teaches patterns but doesn't provide a plan execution engine — requires custom implementation or framework integration"],"requires":["Understanding of prompt engineering and few-shot learning","Familiarity with parsing structured outputs (JSON, YAML) from LLMs","Knowledge of error handling and replanning strategies"],"input_types":["complex user goals","context and constraints","execution feedback and error messages","plan specifications in structured format"],"output_types":["step-by-step reasoning traces","executable plans with substeps","progress tracking and completion status","replanning decisions when steps fail"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_5","uri":"capability://planning.reasoning.metacognition.pattern.for.agent.self.reflection.and.improvement","name":"metacognition-pattern-for-agent-self-reflection-and-improvement","description":"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.","intents":["I want agents to evaluate their own outputs and identify errors before returning results","I need agents to ask clarifying questions when user intent is ambiguous","I want agents to verify facts and acknowledge uncertainty rather than hallucinating","I need agents to learn from feedback and improve over time"],"best_for":["teams building high-stakes agents (medical, legal, financial) where accuracy is critical","developers creating agents that need to handle ambiguous or incomplete user requests","organizations implementing human-in-the-loop systems where agents can escalate uncertain decisions"],"limitations":["Self-reflection adds multiple LLM calls — typically 2-3x the cost and latency of single-pass responses","Agents can't reliably detect all errors through self-critique — hallucinations and factual errors may persist","Curriculum teaches patterns but doesn't provide automated evaluation metrics — requires custom implementation or external evaluation services"],"requires":["Understanding of prompt engineering for self-critique","Familiarity with feedback mechanisms and iterative refinement","Knowledge of uncertainty quantification and confidence scoring"],"input_types":["agent outputs to evaluate","user feedback and corrections","context for fact verification","evaluation criteria and rubrics"],"output_types":["self-critique assessments","refined outputs after reflection","confidence scores and uncertainty indicators","clarifying questions for users","feedback for improvement"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_6","uri":"capability://planning.reasoning.framework.comparison.and.selection.guidance.across.autogen.semantic.kernel.and.azure.ai.agent.service","name":"framework-comparison-and-selection-guidance-across-autogen-semantic-kernel-and-azure-ai-agent-service","description":"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).","intents":["I need to choose between AutoGen, Semantic Kernel, and Azure AI Agent Service for my project","I want to understand the architectural differences between agent frameworks","I need to know which framework supports my preferred language (Python, .NET, etc.)","I want to see how the same agent pattern looks in different frameworks"],"best_for":["technical architects evaluating agent frameworks for production adoption","teams deciding between open-source and managed service approaches","organizations with existing investments in specific platforms (Azure, open-source ecosystems)"],"limitations":["Comparison is educational and may not reflect latest framework versions — requires verification against current documentation","Code samples show basic patterns but don't cover advanced features or performance characteristics","No quantitative benchmarks (latency, cost, throughput) — comparison is qualitative"],"requires":["Understanding of agent fundamentals and core patterns","Familiarity with at least one of the frameworks or willingness to learn","Access to documentation and code samples for each framework"],"input_types":["framework documentation","code samples in multiple frameworks","use case requirements and constraints"],"output_types":["framework comparison matrices","code samples showing same pattern in different frameworks","framework selection guidance based on requirements","migration paths between frameworks"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_7","uri":"capability://automation.workflow.production.deployment.guidance.with.observability.and.evaluation.frameworks","name":"production-deployment-guidance-with-observability-and-evaluation-frameworks","description":"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.","intents":["I need to deploy agents to production with proper monitoring and observability","I want to measure agent performance and quality in production","I need to implement safety guardrails and prevent harmful outputs","I want to understand the operational costs and scaling characteristics of my agent"],"best_for":["teams moving agents from prototypes to production systems","organizations needing to demonstrate agent quality and reliability to stakeholders","developers implementing agents in regulated industries requiring audit trails and safety measures"],"limitations":["Observability and evaluation patterns are framework-agnostic but implementation details vary by deployment platform","Curriculum teaches patterns but doesn't provide pre-built monitoring dashboards — requires custom implementation or platform-specific tools","Cost optimization strategies are covered conceptually but actual costs depend on LLM provider pricing and usage patterns"],"requires":["Understanding of observability concepts (logging, tracing, metrics)","Familiarity with evaluation frameworks and metrics design","Knowledge of production deployment platforms (Azure, AWS, Kubernetes, etc.)","Access to monitoring and logging infrastructure"],"input_types":["agent execution logs and traces","user feedback and quality assessments","performance metrics and cost data","safety and compliance requirements"],"output_types":["observability dashboards and alerts","evaluation reports and quality metrics","cost analysis and optimization recommendations","safety audit trails and compliance documentation"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_8","uri":"capability://memory.knowledge.context.engineering.and.prompt.optimization.for.agent.reasoning","name":"context-engineering-and-prompt-optimization-for-agent-reasoning","description":"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.","intents":["I want to improve agent reasoning quality by providing better context","I need to manage token budgets while keeping context rich enough for good decisions","I want to understand how to structure system prompts for different agent roles","I need to format retrieved documents so agents can reason about them effectively"],"best_for":["developers optimizing agent performance and reducing token costs","teams implementing agents where reasoning quality is critical","builders creating specialized agents with different roles and responsibilities"],"limitations":["Context engineering is largely empirical — optimal context structure varies by use case and requires experimentation","Curriculum teaches principles but doesn't provide automated context optimization — requires manual tuning or custom tools","Summarization techniques can lose important details — tradeoff between token efficiency and information preservation"],"requires":["Understanding of token counting and context window limits","Familiarity with prompt engineering and few-shot learning","Knowledge of information retrieval and ranking"],"input_types":["raw context (documents, conversation history, system information)","context specifications and constraints","agent role and task definitions","token budget limits"],"output_types":["optimized system prompts","formatted and prioritized context","summarized conversation history","context engineering guidelines and best practices"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--ai-agents-for-beginners__cap_9","uri":"capability://memory.knowledge.agent.memory.systems.and.persistent.state.management","name":"agent-memory-systems-and-persistent-state-management","description":"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.","intents":["I want agents to remember previous conversations and learn from past interactions","I need agents to maintain consistent knowledge across multiple conversations","I want to implement semantic memory so agents can retrieve relevant past experiences","I need to manage memory growth and prevent agents from becoming confused by too much history"],"best_for":["teams building long-running agents that interact with users over time","organizations implementing personalized agents that adapt to individual user preferences","developers creating agents that need to maintain consistency across multiple conversations"],"limitations":["Memory systems add complexity and latency — retrieval and update operations introduce additional database calls","No built-in memory consolidation or forgetting — requires custom logic to manage memory growth","Curriculum teaches patterns but doesn't provide a memory management framework — requires integration with external databases"],"requires":["Understanding of vector databases and semantic search","Familiarity with relational databases or document stores for structured memory","Knowledge of memory retrieval strategies and ranking"],"input_types":["conversation history and user interactions","facts and knowledge to store","memory queries and retrieval requests","memory update and consolidation specifications"],"output_types":["retrieved memories relevant to current task","memory storage and update confirmations","memory consolidation summaries","memory consistency checks and conflict resolution"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ or .NET 6+ depending on chosen implementation path","Basic programming knowledge in Python or C#","API keys for OpenAI, Anthropic, or Azure AI services for framework examples","Git and GitHub access to clone the repository","Understanding of function signatures and JSON schema","API keys for at least one LLM provider (OpenAI, Anthropic, Ollama)","Familiarity with async/await patterns in Python or C#","Understanding of prompt injection attacks and defenses","Familiarity with content filtering and safety evaluation techniques","Knowledge of bias detection and mitigation strategies"],"failure_modes":["Curriculum is educational, not a production framework itself — requires implementing patterns using external frameworks","Jupyter Notebook format limits IDE integration and testing workflows compared to native project templates","No built-in hands-on sandbox environment — learners must set up local development or cloud environments","Curriculum teaches patterns but doesn't provide a unified tool registry abstraction — developers must implement provider-specific logic","No built-in tool validation framework — error handling and schema enforcement must be implemented per use case","Async tool execution patterns are covered but orchestration of parallel tool calls requires external frameworks","System message frameworks can be circumvented through prompt injection — requires additional input validation","Content filtering and output validation add latency and may reject valid outputs","Curriculum teaches patterns but doesn't provide automated safety verification — requires custom evaluation and testing","Language-specific guides may lag behind framework updates — requires verification against current framework documentation","builder identity is not verified yet","artifact is still pending review"],"rank_breakdown":{"adoption":0.8862133340557228,"quality":0.25,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"pending_review","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-04-30T11:25:22Z"},"community":{"stars":60380,"forks":20442,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--ai-agents-for-beginners","compare_url":"https://unfragile.ai/compare?artifact=microsoft--ai-agents-for-beginners"}},"signature":"zhicm3ghiVF3Zx6y9OSonXYqMCJvZfvPHI/FIHZGVi9QC+24IItnNxmMW3OADwyTb1CDh4BblPuqr1zKZHbwDw==","signedAt":"2026-06-21T06:26:15.464Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--ai-agents-for-beginners","artifact":"https://unfragile.ai/microsoft--ai-agents-for-beginners","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--ai-agents-for-beginners","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}