Five‑Layer AI Multi‑Agent Architecture: Hierarchical, Human‑in‑the‑Loop, Decentralized, Pipeline, and Data Transformation

1. Hierarchical architecture (vertical command chain): The commander Agent coordinates the overall system and dispatches specialized Agents to execute tasks. Example: enterprise intelligence analysis system with Data Agent extracting financial reports, Search Agent crawling competitor information, and Personal Agent generating risk alerts from emails and meeting minutes.

2. Human‑in‑the‑loop architecture (security barrier): Sensitive operations require manual review, such as medical diagnosis or financial transactions. Example: cancer treatment plan generation where AI provides an initial recommendation, which is then validated by senior physicians before execution.

3. Network architecture (decentralized collaboration): Agents connect peer‑to‑peer forming a distributed decision network. Example: autonomous vehicle fleet coordination where perception, routing, and communication Agents exchange real‑time data without a central scheduler.

4. Pipeline architecture (industrial‑grade precision): Tasks are passed down sequentially like a factory line. Retrieval Agent pulls knowledge from a vector database, Enhancement Agent enriches it with live web data, and Generation Agent produces the final answer, e.g., a legal consultation report.

5. Data transformation architecture (information alchemy): Specialized Agents deeply process raw data. In an e‑commerce data platform, a Cleaning Agent removes invalid orders, an Enhancement Agent enriches user profiles, and a Prediction Agent generates GMV forecasts.

Additional concepts include four combination techniques—loop (AI self‑iteration), parallel (multiple Agents handling subtasks), routing (dynamic Agent selection), and aggregation (knowledge fusion)—which together strengthen the AI thinking engine.