We Built a Production-Ready Deep Research Agent Live. Here's How It Works.

A recent live session introduced durable endpoints, a feature that simplifies building robust API endpoints by integrating durability directly into HTTP handlers, eliminating the need for job queues or state management infrastructure. Traditional durable execution models like Inngest, Temporal, and Azure Durable Functions focus on asynchronous background workflows, but many real-time API endpoints fail to fit this model, especially when involving multiple stages of AI processing, leading to reliability issues. Durable endpoints address these challenges by allowing steps within an API handler to be individually retried if they fail, thus maintaining state and avoiding redundant operations, which is particularly cost-effective for expensive processes like LLM calls. During the demo, durable endpoints were used to develop a deep research agent that efficiently handled complex AI workflows in real-time while providing full observability and seamless retry mechanisms. The session's example code is open source and available on GitHub, with upcoming features including streaming support and advanced flow control mechanisms.

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