AI Skill Report Card
Architecting AI Native Companies
Quick Start14 / 15
Input: Business idea or problem Output: Complete AI-native company blueprint with agent architecture
Python# Company Blueprint Template company_blueprint = { "vision": "One-line problem + AI-native solution", "agent_architecture": { "core_agents": ["planner", "executor", "critic", "optimizer"], "specialized_agents": ["domain-specific agents"], "orchestration": "workflow_engine" }, "tech_stack": ["python", "fastapi", "postgres", "redis", "vector_db"], "repo_structure": "monorepo with agent modules", "mvp_timeline": "2-4 weeks" }
Recommendation▾
Shorten the repository structure section - it's too verbose for what Claude needs
Workflow15 / 15
Phase 1: Concept → Architecture (Day 1-2)
- Define core value proposition and AI advantage
- Map human workflows → agent workflows
- Design agent collaboration patterns
- Choose tech stack and infrastructure
Phase 2: Foundation (Week 1)
- Set up monorepo with agent framework
- Implement core orchestration engine
- Build primary agents (planner, executor, critic)
- Create CI/CD pipeline with agent testing
Phase 3: MVP (Week 2-3)
- Implement specialized domain agents
- Build user interface and API layer
- Deploy agent coordination system
- Add monitoring and logging
Phase 4: Scale (Week 4+)
- Optimize agent performance and costs
- Add feedback loops and learning systems
- Implement horizontal scaling
- Build agent marketplace/ecosystem
Recommendation▾
Reduce the 'Best Practices' section by removing obvious points like 'single responsibility' that Claude already knows
Repository Structure
/ai-native-company
├── agents/
│ ├── core/ # Universal agents (planner, critic, etc.)
│ ├── domain/ # Business-specific agents
│ └── orchestration/ # Workflow engine
├── infrastructure/
│ ├── docker/
│ ├── k8s/
│ └── ci-cd/
├── api/
│ ├── fastapi_app/
│ └── websockets/
├── data/
│ ├── vector_store/
│ └── databases/
├── tests/
│ ├── agent_tests/
│ └── integration/
└── docs/
├── agent_specs/
└── workflows/
Agent Architecture Patterns
Core Agent Framework:
Pythonclass AgentOrchestrator: def __init__(self): self.agents = { 'planner': PlannerAgent(), # Breaks down goals 'executor': ExecutorAgent(), # Implements solutions 'critic': CriticAgent(), # Reviews and validates 'optimizer': OptimizerAgent() # Improves performance } async def execute_workflow(self, goal): plan = await self.agents['planner'].create_plan(goal) result = await self.agents['executor'].execute(plan) feedback = await self.agents['critic'].review(result) return await self.agents['optimizer'].improve(result, feedback)
Specialized Agents by Domain:
- Code Agents:
CodeGeneratorAgent,CodeReviewAgent,TestGeneratorAgent - Business Agents:
CustomerAgent,SalesAgent,AnalyticsAgent - Operations Agents:
DeploymentAgent,MonitoringAgent,SecurityAgent
Examples18 / 20
Example 1: SaaS Idea → AI Company Input: "Build a customer support automation platform" Output:
YAMLVision: "Replace human support teams with AI agents that handle 95% of customer queries" Core Agents: - QueryClassifierAgent: Routes incoming requests - KnowledgeAgent: Searches docs/FAQs/tickets - ResponseAgent: Generates personalized responses - EscalationAgent: Handles complex cases - LearningAgent: Improves from interactions Tech Stack: FastAPI, Postgres, Redis, Pinecone, OpenAI API MVP: 3 weeks (agent framework + basic UI) Scale Plan: Multi-tenant, agent marketplace, white-label
Example 2: Technical Architecture Input: "How should agents collaborate on code generation?" Output:
Python# Agent Pipeline for Code Generation pipeline = [ SpecificationAgent(), # Converts requirements → detailed specs ArchitectureAgent(), # Designs system architecture CodeGeneratorAgent(), # Writes implementation TestGeneratorAgent(), # Creates comprehensive tests ReviewAgent(), # Code review and optimization ValidationAgent(), # End-to-end testing DeploymentAgent() # Automated deployment ]
Recommendation▾
Compress the 'Common Pitfalls' section - many items are basic software engineering principles Claude understands
Best Practices
Agent Design:
- Single responsibility per agent
- Async communication via message queues
- Stateless agents with persistent context storage
- Built-in error handling and retry logic
Orchestration:
- Use workflow engines (Temporal, Airflow, or custom)
- Implement circuit breakers for agent failures
- Add monitoring and observability from day one
- Design for horizontal scaling
Development:
- Agent-first development: build agents before UIs
- Continuous integration with agent testing
- Version control for agent configurations
- A/B testing for agent performance
Business Strategy:
- Start with highest-ROI workflows
- Measure agent performance vs human baseline
- Build network effects through agent collaboration
- Plan agent marketplace from early stage
Common Pitfalls
Architecture Mistakes:
- Building monolithic agents instead of specialized ones
- Tight coupling between agents
- No fallback mechanisms for agent failures
- Ignoring latency and cost optimization
Business Mistakes:
- Automating low-value workflows first
- Not measuring agent vs human performance
- Underestimating human change management
- Building agents without clear success metrics
Technical Mistakes:
- No proper testing framework for agents
- Inadequate logging and debugging tools
- Poor error handling in agent chains
- Not planning for scale from the beginning
Scaling Issues:
- Agent coordination complexity
- Cost explosion without optimization
- Knowledge management across agents
- Maintaining agent consistency at scale
Key Metrics
- Agent Performance: Task completion rate, accuracy, speed
- Cost Efficiency: Agent cost vs human cost per task
- System Health: Uptime, latency, error rates
- Business Impact: Revenue per agent, customer satisfaction
- Development Velocity: Time to deploy new agents