Seven SEAS
A practical architecture model for enterprise-grade AI systems. Seven SEAS bridges foundation-model theory and production implementation through a clear engineering path from data ingestion to real-world deployment.
Books
Seven SEAS
Enterprise AI Architecture Framework
Seven SEAS: The 7 Pillars
Master the seven technical pillars every production-grade agentic system must adopt: State & Memory, Tool Calls, Execution Loops, RAG Grounding, Multi-Agent Collaboration, Guardrails, and Observability.
Chapter Index
The Seven Technical Pillars
1. State & Memory
Production agents need tiered memory that blends working state, episodic history, and retrievable semantic knowledge — with tenant isolation, compaction, and governance built in.
2. Tool Calls
Enterprise reliability depends on strict schemas and standardized protocols that bridge probabilistic model output with deterministic API execution.
3. Execution Loop
True agents plan, execute, evaluate outcomes, and adapt strategies when intermediate steps fail.
4. RAG Grounding
Production systems must retrieve factual, context-specific data in real time to reduce hallucinations — and the state of the art has moved well beyond naive vector search.
5. Multi-Agent
Complex systems perform best when specialized agents collaborate through structured delegation — and the industry is standardizing how they communicate.
6. Guardrails
Autonomous systems require robust security, policy boundaries, and governance before any sensitive action is executed — and modern practice treats safety as a layered, measurable system.
7. Observability
Because agent execution is non-deterministic, deep tracing, eval-driven development, and cost-aware observability are mandatory for production AI.