# Building Scalable AI Agents with TypeScript and PostgreSQL
Artificial Intelligence applications are evolving rapidly from basic chat interfaces into autonomous multi-agent systems capable of executing complex workflows. In this article, we explore how to design production-ready AI agents using TypeScript, Express, and PostgreSQL pgvector.
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## 1. Architectural Overview
When engineering AI-powered features, maintaining a clear separation between data storage, vector indexing, and orchestration is essential.
### Key Pillars:
- Vector Storage: Storing high-dimensional embeddings directly in PostgreSQL using pgvector.
- Type-Safe API Contracts: Ensuring all tool inputs and outputs are validated via Zod schemas.
- Resilient Execution: Wrapping asynchronous AI calls with timeouts and deterministic error handling.
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## 2. Storing Embeddings in PostgreSQL
Rather than managing a separate vector database cluster, PostgreSQL with pgvector allows you to store structured tabular data alongside semantic embeddings in a single database.
```typescript
import { pgTable, text, vector } from "drizzle-orm/pg-core";
export const knowledgeEmbeddings = pgTable("knowledge_embeddings", {
id: text("id").primaryKey(),
content: text("content").notNull(),
embedding: vector("embedding", { dimensions: 1536 }),
});