Building observable AI agents with Temporal

AI agents are increasingly complex in orchestrating multiple models and executing multi-step workflows, necessitating robust infrastructure for durability and observability. The integration of Braintrust and Temporal offers a solution by combining durable execution with LLM observability, addressing challenges such as mid-task failures and debugging across multiple API calls. Temporal ensures durable workflow execution with automatic retries and state persistence, while Braintrust provides LLM call tracing and prompt management. This integration is exemplified in a deep research agent that plans, searches, and synthesizes information, benefiting from Temporal's resilience to failures and Braintrust's visibility into agent behavior. The system enables seamless prompt versioning and cost tracking for efficient workflow management, which is already being used by major AI applications like OpenAI and Scale AI.