AI Architecture

What is AI architecture?

AI architecture is a multi-layered discipline that structures how data, models, automation, and interfaces work together to deliver intelligent capabilities in enterprises and design fields such as architecture. Topics span from foundational data management through advanced design optimization, automation, and user experience—all powered by emerging AI technologies.

AI architecture refers to the structured design, components, and governance needed to build, deploy, and manage artificial intelligence systems. You can think of it as the “blueprint” that defines how AI fits into the wider enterprise ecosystem — technically, organizationally, and strategically.

It spans three main layers:

1. Conceptual Layer (Why & What)

• Vision & Purpose: How AI aligns with business goals (efficiency, decision support, automation, augmentation).
• Governance & Ethics: GRC (Governance, Risk, Compliance), policies for transparency, fairness, explainability.
• Integration into Enterprise Architecture: How AI fits into TOGAF domains (Business, Data, Application, Technology).

2. Logical Layer (How it’s Organized)

• This is about the core building blocks of an AI system:
• Data Layer: Data sources, pipelines, data governance, knowledge graphs.
• Model Layer: Training, fine-tuning, and serving of machine learning and LLM models.
• Application Layer: Interfaces, APIs, agents, copilots, and embedding AI into business processes.
• MLOps / AIOps: Continuous delivery, monitoring, and retraining pipelines.
• Security & Compliance: Data protection, model security, responsible AI checks.

3. Physical Layer (With What)

• The actual technology stack:
• Infrastructure: GPUs, TPUs, NPUs, cloud vs. edge deployment.
• Frameworks & Platforms: TensorFlow, PyTorch, LangChain, Azure AI, OpenAI APIs, Ollama, etc.
• Tools: Monitoring dashboards, annotation tools, vector databases, semantic search.
• Deployment Models: Cloud-native, on-premise, hybrid, or federated setups.