Building Reliable Support Agents Using the Effect Typescript Library - Michael Fester
Jun 3, 2025 · 7:22
Michael Fester, co-founder and CTO of Fourteen.ai, explains how his team built production-ready AI support agents using the Effect TypeScript library, arguing it provides strong type safety, composability, and reliability for systems that rely on LLMs. The architecture uses Effect across the entire stack, including Effect RPC, Effect HTTP, Effect SQL, and a custom DSL for agent workflows. Reliability features include fallback between LLM providers (e.g., GPT-4.0 mini to Gemini Flash 2.0), retry policies with state tracking, and duplicated token streams for analytics. Dependency injection allows easy mocking of LLM providers for testing. However, Fester warns of pitfalls like silently losing errors from upstream catches and the steep learning curve. He recommends incremental adoption, starting with a single service.
