Overview
AI application architecture defines how user interfaces, orchestration logic, models, data services, and controls fit together to deliver reliable business outcomes.
Architecture Layers
- experience layer for user and system interactions
- orchestration layer for workflow and tool routing
- intelligence layer for models and retrieval
- data and observability layer for reliability and auditability
Key Decisions
- monolith vs modular service boundaries
- synchronous vs asynchronous workflow execution
- RAG, fine-tuning, or hybrid intelligence pattern
- control points for policy, fallback, and human handoff
Design Anti-Patterns
- starting from model choice rather than workflow design
- missing architecture for failure and degraded modes
- no versioning for prompts and orchestration logic
- shipping without observability and quality evaluation
Related Guides
- AI Decision Engine complete guide: https://aicreationlabs.com/ai-decision-engine/complete-guide
- AI implementation roadmap: https://aicreationlabs.com/frameworks/ai-implementation-roadmap
- How to design AI architecture: https://aicreationlabs.com/guides/how-to-design-ai-architecture
- AI governance framework: https://aicreationlabs.com/frameworks/ai-governance-framework
- How to choose AI platform: https://aicreationlabs.com/guides/how-to-choose-ai-platform
References
- Google Architecture Framework: https://cloud.google.com/architecture/framework
- AWS Well-Architected: https://aws.amazon.com/architecture/well-architected/
- OpenAI production best practices: https://platform.openai.com/docs/guides/production-best-practices
Talk to an AI Implementation Expert
If you want a decision review for this topic, book a strategy session.
Book a call: https://calendly.com/ai-creation-labs/30-minute-chatgpt-leads-discovery-call
We can cover:
- decision criteria and tradeoffs
- risk and control requirements
- implementation plan
- KPI framework