How to build deep research agents using Temporal and Braintrust

Deep research agents, designed to generate cited, evidence-based reports from complex queries, face significant challenges due to their intricate architecture involving multiple large language model (LLM) calls, parallel web searches, and synthesis steps. These processes are prone to failures such as API timeouts, partial search successes, and nondeterministic outputs, which necessitate infrastructure-level solutions for resilience. Collaborative efforts by Braintrust and Temporal focus on enhancing these agents with Durable Execution and observability, employing multi-agent pipelines that decompose research questions, optimize search queries, execute web searches, and synthesize findings into comprehensive reports. Temporal's framework allows each agent to function as a retryable unit, ensuring continuity despite failures, and Braintrust's integration provides detailed tracing and evaluation capabilities, facilitating systematic improvements and reducing manual interventions. This approach not only addresses common failure modes but also establishes a feedback loop for continuous quality enhancement, as demonstrated in the shared Braintrust cookbook for implementation.