{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop","slug":"iusztinpaul--designing-real-world-ai-agents-workshop","name":"designing-real-world-ai-agents-workshop","type":"template","url":"https://academy.towardsai.net/courses/agent-engineering?utm_source=github&utm_medium=aieng&utm_campaign=2026_aieng_workshop&utm_id=researchwriter","page_url":"https://unfragile.ai/iusztinpaul--designing-real-world-ai-agents-workshop","categories":["automation"],"tags":["ai-agent","ai-skills","ai-workflow","deep-research","mcp","multi-agent-systems","workshop"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_0","uri":"capability://search.retrieval.gemini.grounded.iterative.research.with.google.search.integration","name":"gemini-grounded iterative research with google search integration","description":"Executes multi-turn research workflows using Google Gemini API with built-in Google Search grounding to retrieve factual, up-to-date information. The Deep Research Agent (src/research/server.py) implements a tool-use pattern where Gemini can invoke search tools iteratively, refining queries based on intermediate results, and persists findings into a structured research.md file. Supports YouTube transcript extraction when URLs are provided, enabling multi-modal source integration.","intents":["I need to gather current, factually-grounded information on a topic without hallucination","I want to automate research workflows that iterate on search queries based on what's discovered","I need to extract and structure research findings from multiple sources including video transcripts"],"best_for":["Teams building content pipelines requiring factual accuracy (journalism, technical writing, marketing)","Developers implementing agentic systems that need grounded search without external RAG infrastructure","Organizations automating research-to-content workflows at scale"],"limitations":["Requires Google Gemini API key and active Google Search grounding subscription — not free tier compatible","YouTube transcript extraction limited to publicly available transcripts; no support for age-restricted or private videos","Research depth constrained by Gemini context window (~32k tokens) — very large research topics may require chunking","No built-in deduplication of search results across iterations — may retrieve redundant information"],"requires":["Python 3.12+","Google Gemini API key with Search grounding enabled","FastMCP framework (pyproject.toml dependency)","Pydantic 2.x for data validation","MCP-compatible client (Claude Code, Cursor, or custom harness)"],"input_types":["text (topic/research query)","URL (for YouTube transcript extraction)","structured research guidelines (Pydantic models)"],"output_types":["markdown file (research.md with structured findings)","JSON (research metadata and source citations)","text (iterative search queries and results)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_1","uri":"capability://tool.use.integration.mcp.based.multi.agent.orchestration.with.decoupled.server.architecture","name":"mcp-based multi-agent orchestration with decoupled server architecture","description":"Implements a two-server MCP architecture (Deep Research Agent + LinkedIn Writer Agent) using FastMCP framework, where each server exposes tools, resources, and prompts independently and communicates through standardized MCP protocol. The architecture decouples research and writing concerns, allowing each agent to be developed, tested, and scaled independently while maintaining a unified interface. Configuration is managed via .mcp.json and environment variables, enabling runtime server discovery and tool registration.","intents":["I want to build modular AI agents that can be developed and tested independently but orchestrated together","I need a production-grade pattern for multi-agent systems that avoids monolithic code","I want to expose agent capabilities as reusable tools/resources that can be consumed by different clients"],"best_for":["Teams building complex agentic workflows with multiple specialized agents","Developers migrating from monolithic LLM applications to modular, composable architectures","Organizations standardizing on MCP for AI tool integration across multiple products"],"limitations":["MCP protocol overhead adds ~50-100ms per tool invocation due to serialization and IPC","Debugging multi-server workflows requires tracing across process boundaries — standard debuggers insufficient","No built-in load balancing or failover for MCP servers — requires external orchestration (Docker, Kubernetes)","State sharing between servers must be explicit (file-based or external store) — no shared in-memory state"],"requires":["Python 3.12+","FastMCP framework (pyproject.toml: fastmcp>=0.1.0)","MCP-compatible client harness (Claude Code, Cursor, or custom implementation)",".mcp.json configuration file with server definitions","Environment variables for API keys and server endpoints"],"input_types":["MCP tool definitions (JSON schema)","MCP resources (markdown, text files)","MCP prompts (system/user message templates)","Configuration files (.mcp.json, .env)"],"output_types":["Tool execution results (JSON, text, markdown)","Resource content (structured data, markdown)","Prompt templates (rendered with context)","Server metadata (capabilities, schema)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_10","uri":"capability://automation.workflow.configuration.management.with.environment.variables.and.pydantic.settings","name":"configuration management with environment variables and pydantic settings","description":"Centralizes configuration using Pydantic Settings models (src/research/config/, src/writing/config/) that load from environment variables and .env files, enabling environment-specific configuration without code changes. Configuration includes API keys, model parameters, evaluation thresholds, and server endpoints. Pydantic validation ensures type safety and provides helpful error messages for missing or invalid configuration.","intents":["I want to configure API keys, model parameters, and thresholds without editing code","I need different configurations for development, testing, and production environments","I want validation errors for missing configuration to fail fast at startup"],"best_for":["Teams deploying agents across multiple environments (dev, staging, prod)","Developers managing sensitive configuration (API keys) securely","Organizations standardizing on environment-based configuration"],"limitations":["Environment variables are limited to string values — complex nested configuration requires JSON encoding","Pydantic Settings validation happens at import time — errors may not surface until first agent invocation",".env files are not suitable for production (security risk) — requires external secret management (AWS Secrets Manager, etc.)","No built-in configuration versioning or rollback — changes require redeployment"],"requires":["Python 3.12+","Pydantic 2.x (pyproject.toml: pydantic>=2.0)","Pydantic Settings (pyproject.toml: pydantic-settings>=2.0)",".env file or environment variables set in shell/deployment system"],"input_types":["environment variables (KEY=VALUE)",".env file (KEY=VALUE pairs)","Pydantic Settings model definitions"],"output_types":["validated configuration objects (Python dataclasses)","configuration errors (validation exceptions with helpful messages)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_11","uri":"capability://memory.knowledge.structured.research.persistence.and.markdown.based.knowledge.representation","name":"structured research persistence and markdown-based knowledge representation","description":"Persists research findings to a structured markdown file (research.md) that serves as the knowledge base for the writing agent. The markdown format enables human readability while maintaining machine-parseable structure (headings, lists, citations). Research findings include source citations, timestamps, and iterative search history, creating an auditable record of how conclusions were reached. The writing agent reads this markdown to generate content, ensuring factual grounding.","intents":["I want to persist research findings in a human-readable format that can be manually inspected and edited","I need to maintain citations and source tracking for factual accuracy and credibility","I want the writing agent to have access to structured research context without re-running searches"],"best_for":["Content teams that need to review and edit research before writing","Organizations requiring audit trails for factual claims (journalism, academic writing)","Developers building research-to-content pipelines with human-in-the-loop review"],"limitations":["Markdown parsing is fragile — malformed research.md may break writing agent's ability to extract context","No built-in versioning or diff tracking — multiple research iterations overwrite previous findings","Markdown structure is not standardized — different research agents may produce incompatible formats","Large research documents (>100KB) may exceed LLM context windows when passed to writing agent"],"requires":["Python 3.12+","Writable filesystem for persisting research.md","Markdown parsing library (built-in Python, or external like markdown-it)"],"input_types":["search results (from Gemini Search API)","YouTube transcripts (if URLs provided)","research metadata (timestamps, sources)"],"output_types":["research.md file (markdown with structured sections)","research metadata (JSON with citations and timestamps)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_2","uri":"capability://planning.reasoning.evaluator.optimizer.loop.for.iterative.content.refinement","name":"evaluator-optimizer loop for iterative content refinement","description":"Implements a multi-iteration content generation and evaluation pattern in the LinkedIn Writer Agent (src/writing/server.py) where an LLM generates initial content, an evaluator (LLM-as-judge) scores it against quality criteria, and an optimizer refines it based on feedback. The loop continues until quality thresholds are met or max iterations reached. Uses Opik for tracing and LLM-based evaluation metrics, enabling observable, measurable content quality improvement without human-in-the-loop.","intents":["I want to automatically improve generated content through multiple refinement cycles without manual review","I need to measure content quality objectively using LLM-based evaluation metrics","I want to trace and observe each iteration of content generation for debugging and optimization"],"best_for":["Content teams automating post/article generation with quality guarantees","Developers building self-improving agentic systems that refine outputs iteratively","Organizations implementing LLM-as-judge evaluation at scale with observability"],"limitations":["Each iteration requires 2-3 LLM calls (generation + evaluation + optional optimization), increasing latency and cost by 2-3x vs single-pass generation","LLM-as-judge evaluation is only as good as the evaluation prompt — requires careful prompt engineering and validation","No guarantee of convergence — poorly defined quality criteria may cause infinite loops (mitigated by max_iterations)","Opik integration adds observability overhead (~100-200ms per iteration) and requires external Opik account"],"requires":["Python 3.12+","Google Gemini API key (for generation and evaluation)","Opik API key and account (for tracing and LLM-as-judge metrics)","Writing profiles and prompt templates (src/writing/profiles/)","Quality criteria defined as Pydantic models or evaluation prompts"],"input_types":["text (research content, writing guidelines)","structured profiles (writing style, tone, audience)","evaluation criteria (quality metrics, thresholds)","max_iterations parameter (integer)"],"output_types":["refined text content (LinkedIn post)","evaluation scores (JSON with metric values)","iteration history (trace logs in Opik)","optimization feedback (text explaining changes)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_3","uri":"capability://image.visual.ai.image.generation.with.gemini.imagen.integration","name":"ai image generation with gemini imagen integration","description":"Integrates Google Gemini's Imagen model for AI-generated images within the writing workflow, enabling automatic image creation to accompany generated LinkedIn posts. The image generation is triggered based on post content and writing profiles, with generated images persisted to the dataset directory. Supports prompt engineering for image generation based on post themes and audience preferences.","intents":["I want to automatically generate relevant images for social media posts without manual design","I need to create visually consistent content across multiple posts using AI image generation","I want to include AI-generated images in my content pipeline without external design tools"],"best_for":["Content creators automating LinkedIn post generation with visual assets","Marketing teams scaling content production with AI-generated imagery","Developers building end-to-end content pipelines that include visual media"],"limitations":["Imagen quality and style consistency varies based on prompt engineering — requires iteration to achieve desired aesthetic","Image generation adds 5-10 seconds latency per post (Imagen API response time)","No fine-tuning or style transfer — limited control over visual consistency across multiple images","Generated images may require manual review for brand compliance or content policy violations"],"requires":["Python 3.12+","Google Gemini API key with Imagen model access","Writing profiles with image generation prompts (src/writing/profiles/)","Writable dataset directory for persisting generated images"],"input_types":["text (LinkedIn post content)","structured profile (writing style, audience, brand guidelines)","image generation prompt (derived from post content)"],"output_types":["PNG/JPEG image file (persisted to datasets/)","image metadata (filename, generation timestamp, prompt used)","image URL or local path reference"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_4","uri":"capability://data.processing.analysis.dataset.driven.evaluation.with.llm.as.judge.metrics","name":"dataset-driven evaluation with llm-as-judge metrics","description":"Implements a structured dataset system (datasets/ directory) with batch evaluation scripts that process multiple content samples through the writing workflow and score them using LLM-as-judge metrics via Opik. The evaluation system measures quality across dimensions (clarity, engagement, relevance) and aggregates results for statistical analysis. Supports dataset versioning and comparison across model versions or writing profiles.","intents":["I want to evaluate content quality across multiple samples systematically, not just spot-check individual posts","I need to measure how different writing profiles or model versions affect content quality","I want to track quality improvements over time as I refine prompts and evaluation criteria"],"best_for":["Teams running A/B tests on writing profiles or content strategies","Researchers evaluating agentic content generation systems quantitatively","Organizations building quality dashboards for AI-generated content"],"limitations":["Batch evaluation requires running full workflow for each sample — 5-10 minutes per sample with Opik tracing overhead","LLM-as-judge metrics are subjective and require careful prompt validation — results may not correlate with human judgment","Dataset management is manual (CSV/JSON files) — no built-in versioning or experiment tracking beyond Opik","Evaluation costs scale linearly with dataset size — 100 samples = 200-300 LLM API calls"],"requires":["Python 3.12+","Google Gemini API key","Opik API key and account","Dataset files (CSV or JSON) in datasets/ directory","Evaluation metric definitions (Pydantic models or prompts)"],"input_types":["CSV/JSON dataset files (topics, writing guidelines, expected outputs)","evaluation criteria (quality dimensions, scoring rubrics)","writing profiles (for A/B testing different styles)"],"output_types":["evaluation results (JSON with scores per sample)","aggregated metrics (mean, std dev, percentiles)","comparison reports (profile A vs profile B)","Opik trace links (for detailed iteration inspection)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_5","uri":"capability://tool.use.integration.mcp.tool.and.resource.definition.with.schema.based.routing","name":"mcp tool and resource definition with schema-based routing","description":"Defines MCP tools and resources using FastMCP decorators (@mcp.tool, @mcp.resource) with JSON schema validation, enabling type-safe tool invocation and automatic schema generation. The research and writing servers expose distinct tool sets (search, research persistence, content generation, evaluation) with Pydantic-based input/output validation. MCP routers (src/research/routers/, src/writing/routers/) map tool invocations to application logic, decoupling tool definitions from implementation.","intents":["I want to expose agent capabilities as strongly-typed MCP tools that clients can discover and invoke safely","I need to validate tool inputs/outputs automatically without writing boilerplate validation code","I want to decouple tool definitions (schema) from implementation (business logic)"],"best_for":["Teams building MCP servers that need to expose multiple tools with type safety","Developers integrating agents into IDEs (Claude Code, Cursor) that require schema-based tool discovery","Organizations standardizing on Pydantic for data validation across agentic systems"],"limitations":["FastMCP decorators add minimal overhead (~5ms per tool invocation) but require understanding of MCP protocol","JSON schema generation from Pydantic models may not capture all validation constraints (e.g., regex patterns, custom validators)","Tool routing logic must be manually implemented in routers/ — no automatic routing based on tool name","No built-in rate limiting or quota management per tool — requires external middleware"],"requires":["Python 3.12+","FastMCP framework (pyproject.toml: fastmcp>=0.1.0)","Pydantic 2.x for data models and validation","MCP-compatible client (Claude Code, Cursor, or custom harness)"],"input_types":["Pydantic model definitions (for tool input/output schemas)","FastMCP decorator parameters (tool name, description, input_schema)"],"output_types":["JSON schema (auto-generated from Pydantic models)","MCP tool definitions (discoverable by clients)","validated tool results (typed Python objects)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_6","uri":"capability://text.generation.language.prompt.template.system.with.writing.profiles.and.context.injection","name":"prompt template system with writing profiles and context injection","description":"Implements a prompt template system (src/writing/profiles/) where writing profiles define tone, style, audience, and quality criteria as structured data, and prompt templates inject these profiles into system/user messages. The system uses Jinja2-style templating (or similar) to dynamically construct prompts based on profile attributes and research content. Profiles are versioned and can be A/B tested to measure impact on content quality.","intents":["I want to define reusable writing styles/personas that can be applied consistently across multiple posts","I need to A/B test different writing profiles to see which produces higher-quality content","I want to separate prompt logic (templates) from profile data (configuration)"],"best_for":["Content teams managing multiple writing styles or brand voices","Researchers running controlled experiments on prompt engineering","Organizations scaling content generation with consistent quality across profiles"],"limitations":["Profile effectiveness depends on prompt engineering quality — poorly designed profiles may not produce desired results","No automatic profile discovery or recommendation — profiles must be manually created and tested","Template rendering adds ~10-20ms per prompt construction (negligible but measurable)","Profile versioning is manual (file-based) — no built-in experiment tracking or rollback"],"requires":["Python 3.12+","Writing profile definitions (YAML, JSON, or Pydantic models in src/writing/profiles/)","Prompt template files (Jinja2 or similar templating syntax)","Research content (markdown or structured data) for context injection"],"input_types":["writing profile (structured data: tone, style, audience, quality criteria)","research content (markdown or text)","template variables (topic, keywords, target audience)"],"output_types":["rendered prompt (system message + user message with injected context)","profile metadata (name, version, description)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_7","uri":"capability://automation.workflow.end.to.end.workflow.orchestration.from.research.to.published.content","name":"end-to-end workflow orchestration from research to published content","description":"Orchestrates a complete workflow (src/research/server.py → src/writing/server.py) where research findings are automatically fed into the writing agent, which generates, evaluates, and refines content, then generates accompanying images. The workflow is exposed as a high-level skill (.claude/skills/research-and-write/SKILL.md) that can be invoked from Claude Code or Cursor with a single topic input. Workflow state is persisted to the filesystem (research.md, generated posts, images), enabling resumption and inspection at any stage.","intents":["I want to go from a topic idea to a published LinkedIn post with image in one automated workflow","I need to inspect intermediate outputs (research, draft posts, evaluation scores) for debugging or manual refinement","I want to run this workflow repeatedly with different topics without manual orchestration"],"best_for":["Content creators automating their entire post creation pipeline","Teams scaling content production from ideation to publication","Developers building end-to-end agentic workflows as reference implementations"],"limitations":["End-to-end latency is 5-15 minutes per topic (research + generation + evaluation + image generation) — not suitable for real-time content needs","Workflow is sequential (research → write → evaluate → image) — no parallelization of independent steps","Failure at any stage (API timeout, invalid output) stops the entire workflow — no built-in retry or error recovery","Filesystem-based state management is not suitable for concurrent workflows — requires external state store for production"],"requires":["Python 3.12+","Google Gemini API key with Search grounding and Imagen access","Opik API key (for evaluation tracing)","MCP-compatible client (Claude Code, Cursor)","Writable filesystem for persisting research.md, posts, and images"],"input_types":["text (topic or research query)","optional: writing profile name (defaults to 'default')","optional: max_iterations for evaluation loop"],"output_types":["research.md (structured research findings)","generated LinkedIn post (text)","evaluation scores (JSON)","AI-generated image (PNG/JPEG)","workflow trace (Opik links)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_8","uri":"capability://automation.workflow.observability.and.tracing.with.opik.integration","name":"observability and tracing with opik integration","description":"Integrates Opik for end-to-end tracing of agent workflows, capturing every LLM call, tool invocation, and evaluation metric. Opik traces are automatically generated for research iterations, content generation cycles, and evaluation steps, with links persisted in output metadata. The system enables post-hoc analysis of agent behavior, debugging of failed workflows, and measurement of cost/latency across workflow stages.","intents":["I want to see exactly what LLM calls and tool invocations happened in my workflow for debugging","I need to measure latency and cost breakdown across research, writing, and evaluation stages","I want to compare traces across different writing profiles or model versions to understand performance differences"],"best_for":["Teams debugging complex agentic workflows with multiple LLM calls","Developers optimizing agent performance (latency, cost, quality)","Organizations monitoring production agentic systems for quality and cost"],"limitations":["Opik integration adds 100-200ms overhead per workflow due to trace serialization and API calls","Opik requires external account and API key — adds operational dependency","Trace data is stored in Opik cloud — requires data residency compliance for sensitive workflows","Opik UI is web-based — no local inspection of traces without internet access"],"requires":["Python 3.12+","Opik Python SDK (pyproject.toml: opik>=0.1.0)","Opik API key and account","Network access to Opik cloud API"],"input_types":["LLM calls (model, prompt, response)","tool invocations (tool name, inputs, outputs)","evaluation metrics (metric name, score, reasoning)"],"output_types":["Opik trace URL (shareable link to full workflow trace)","trace metadata (latency, cost, token counts)","evaluation results (scores, feedback)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iusztinpaul--designing-real-world-ai-agents-workshop__cap_9","uri":"capability://automation.workflow.workflow.test.scripts.and.batch.processing.automation","name":"workflow test scripts and batch processing automation","description":"Provides Python scripts (scripts/test_research_workflow.py, batch dataset processing scripts) that automate end-to-end testing and evaluation of the multi-agent system. Scripts handle dataset loading, workflow invocation, result collection, and metric aggregation. Uses GNU Make (Makefile) for task orchestration, enabling developers to run complex workflows with simple commands (e.g., `make test-research`, `make evaluate-dataset`).","intents":["I want to test the entire research-to-content workflow programmatically without manual invocation","I need to run batch evaluation across a dataset of topics and collect results systematically","I want to automate repetitive tasks (dataset processing, metric calculation) with simple commands"],"best_for":["Developers testing agentic workflows in CI/CD pipelines","Teams running batch evaluations on datasets","Researchers automating experimental workflows"],"limitations":["Scripts are tightly coupled to project structure (src/, datasets/, scripts/) — not easily reusable in other projects","Batch processing is sequential — no parallelization across samples (would require async/multiprocessing refactoring)","Make-based task orchestration is shell-dependent — less portable than Python-based task runners (Invoke, Taskipy)","No built-in error handling or retry logic — failed samples stop the batch process"],"requires":["Python 3.12+","GNU Make (for Makefile task orchestration)","All dependencies from pyproject.toml","Dataset files in datasets/ directory","Environment variables configured (.env file)"],"input_types":["dataset files (CSV/JSON with topics and metadata)","Makefile targets (test-research, evaluate-dataset, etc.)","command-line arguments (optional: sample count, profile name)"],"output_types":["test results (pass/fail, error logs)","evaluation metrics (JSON with scores per sample)","generated content (posts, images, research files)","aggregated reports (mean scores, comparisons)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.12+","Google Gemini API key with Search grounding enabled","FastMCP framework (pyproject.toml dependency)","Pydantic 2.x for data validation","MCP-compatible client (Claude Code, Cursor, or custom harness)","FastMCP framework (pyproject.toml: fastmcp>=0.1.0)","MCP-compatible client harness (Claude Code, Cursor, or custom implementation)",".mcp.json configuration file with server definitions","Environment variables for API keys and server endpoints","Pydantic 2.x (pyproject.toml: pydantic>=2.0)"],"failure_modes":["Requires Google Gemini API key and active Google Search grounding subscription — not free tier compatible","YouTube transcript extraction limited to publicly available transcripts; no support for age-restricted or private videos","Research depth constrained by Gemini context window (~32k tokens) — very large research topics may require chunking","No built-in deduplication of search results across iterations — may retrieve redundant information","MCP protocol overhead adds ~50-100ms per tool invocation due to serialization and IPC","Debugging multi-server workflows requires tracing across process boundaries — standard debuggers insufficient","No built-in load balancing or failover for MCP servers — requires external orchestration (Docker, Kubernetes)","State sharing between servers must be explicit (file-based or external store) — no shared in-memory state","Environment variables are limited to string values — complex nested configuration requires JSON encoding","Pydantic Settings validation happens at import time — errors may not surface until first agent invocation","builder identity is not verified yet","artifact is still pending review"],"rank_breakdown":{"adoption":0.151748694006156,"quality":0.39,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"pending_review","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:55.150Z","last_commit":"2026-04-30T13:54:57Z"},"community":{"stars":202,"forks":57,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=iusztinpaul--designing-real-world-ai-agents-workshop","compare_url":"https://unfragile.ai/compare?artifact=iusztinpaul--designing-real-world-ai-agents-workshop"}},"signature":"0Jo0g704tcjxldgnn2S2yg2KR54qyVRtycbWxTxtdVTvmee19aQrr9QsfKhNtpOhLQ+1/N65m7p/GP8CPF4KCw==","signedAt":"2026-06-23T05:33:00.249Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/iusztinpaul--designing-real-world-ai-agents-workshop","artifact":"https://unfragile.ai/iusztinpaul--designing-real-world-ai-agents-workshop","verify":"https://unfragile.ai/api/v1/verify?slug=iusztinpaul--designing-real-world-ai-agents-workshop","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"}}