About This Architecture

Four-layer AI system architecture orchestrating multiple specialized agents—Coordinator, RAG, Execution, and Recommendation—across Orchestrator, AI Agent, Algorithm/Model, and Data layers on AWS. The Workflow Manager and Scheduler route tasks through the Coordinator Agent, which delegates to RAG Agent (leveraging LLM and embeddings), Execution Agent (running classifiers and rankers), and Recommendation Agent (applying rule engines). Data flows bidirectionally: LLMs query Pinecone vector DB, embeddings persist to PostgreSQL, ML models consume ETL pipelines, and rule engines access S3 object storage. This layered design separates concerns, enables independent scaling of agents and models, and supports complex agentic workflows with observability via Monitoring and Logging. Fork and customize this diagram on Diagrams.so to adapt agent responsibilities, swap LLM providers, or integrate additional data sources.

People also ask

How do you design a multi-agent AI system architecture on AWS with separate orchestration, agent, model, and data layers?

This 4-layer architecture separates concerns: the Orchestrator Layer (Workflow Manager, Scheduler, Monitoring) routes tasks to the AI Agent Layer (Coordinator, RAG, Execution, Recommendation agents), which invoke the Algorithm/Model Layer (LLMs, embeddings, classifiers, rule engines), all backed by the Data Layer (Pinecone, PostgreSQL, S3, ETL pipelines). This design enables independent scaling, o