build-ai-agents

You build AI agents in a TypeScript backend by defining domain tools, creating agents that wrap business logic, and exposing chat endpoints to orchestrate streaming AI conversations with modular tooling.

The best way to orchestrate streaming chat workflows is by creating agent loops that integrate with your backend TypeScript project and AI models, which reduces boilerplate by handling persistent chats and context-aware responses.

Yes, this approach works with the ai-sdk in TypeScript by integrating the framework to build standard AI models, define modular tools, and expose chat routes for frontend integrations.

You expose chat endpoints in a backend project by creating end-to-end chat routes that wire into your frontend, enabling streaming AI conversations and persistent chats via standard SDKs.

Yes, you can define domain-specific tools and wrap business logic to expose them to a streaming chat interface, allowing your AI agents to automate backend AI conversations contextually.

Using valibot when building AI agents and tools provides schema validation for domain-specific chat workflows, ensuring structured data integration within your TypeScript backend and AI models.