AI Foundations

# The Complete Infrastructure Stack for Agentic AI **Building AI systems that don't fall apart—from the ground up.** I'm building and teaching the complete infrastructure stack for agentic AI applications publicly, explaining every architecture decision along the way. This isn't about frameworks that change every six months. It's about principles that transfer across any technology stack. — ## The Philosophy Most AI tutorials teach you how to call an API. They don't teach you how to build systems that survive contact with production. AI Foundations is different: - **Production-first**: Every pattern is designed for real-world deployment - **Principle-based**: Learn transferable skills, not just tool syntax - **Incremental complexity**: Start simple, add sophistication as needed - **Battle-tested**: These patterns come from building actual systems — ## The Five Guardrails Before building anything sophisticated, you need guardrails. These five disciplines prevent the silent failures that kill AI projects: | Guardrail | Purpose | Key Tools | |--———|———|--———| | **Tests** | Catch bugs before production | pytest, Jest, Playwright | | **Linting** | Enforce code consistency | Ruff, ESLint, Prettier | | **Types** | Prevent runtime errors | Pydantic, TypeScript | | **Logging** | Understand system behavior | Structured logging, OpenTelemetry | | **Architecture** | Maintain system coherence | Clean boundaries, dependency rules | These aren't optional. They're the foundation everything else builds on. [Learn About the Five Guardrails →](https://www.byrddynasty.com/five-guardrails/) — ## The Learning Path ### Level 0: Full-Stack Foundations The production-ready patterns for modern web applications. If you can't build a reliable web app, you can't build a reliable AI app. **What you'll learn:** - FastAPI backend with async patterns and structured error handling - PostgreSQL with proper migrations and connection pooling - React frontend with TypeScript and modern state management - API layer patterns that scale - Testing strategies for full-stack applications **Key principle**: Master the fundamentals before adding AI complexity. — ### Level 0.5: Authentication & Authorization Identity and access management for AI systems. Critical for any production deployment. **What you'll learn:** - JWT-based authentication with secure session handling - Role-Based Access Control (RBAC) for multi-tenant systems - API key management for service-to-service communication - Security best practices for credential storage **Key principle**: Identity is the foundation of trust in autonomous systems. — ### Level 1: AI Agent Core The reusable components every agentic application needs. Build once, use everywhere. **What you'll learn:** - LLM client abstractions that work across providers - Tool frameworks for extending agent capabilities - Agent orchestration patterns (single agent to multi-agent) - Memory and context management systems - Structured output handling with validation **Key principle**: Invest in reusable infrastructure, not one-off implementations. — ### Level 2: Domain Patterns Reusable patterns for specific problem domains. Apply AI to real business challenges. **Domains covered:** - **Investment Analysis**: Financial data pipelines, research automation - **Operations**: Workflow automation, monitoring, alerting - **Customer Service**: Conversational AI, ticket routing, escalation - **Content**: Generation, summarization, personalization **Key principle**: Domain expertise + AI infrastructure = practical value. — ### Level 3: Complete Applications Full agentic AI applications built on the foundation layers. Study real systems, adapt for your needs. **What you'll see:** - Complete working applications with source code - Architecture decisions explained in detail - Trade-offs discussed honestly - Deployment and operations guidance **Key principle**: Theory without practice is incomplete. See how it all comes together. — ## How to Use This Resource **If you're new to AI development:** Start with Level 0. Seriously. The biggest mistakes in AI projects come from weak foundations, not weak models. **If you're an experienced developer:** Skim Level 0, focus on Level 1. The agent core patterns will save you months of trial and error. **If you're building production systems:** Study Level 2 and 3. See how the patterns combine in real applications. — ## Related Resources - [The Nine Skills Framework](https://www.byrddynasty.com/nine-skills/) — The competencies that make AI systems reliable - [Videos](https://www.byrddynasty.com/videos/) — Deep dives on specific topics - [Newsletter](https://www.byrddynasty.com/newsletter/) — Weekly insights on building AI that works — [← Back to Learn](https://www.byrddynasty.com/learn/)