Logging vs. AI observability: Why logs alone aren't enough to monitor AI agents

Braintrust enhances AI observability by going beyond basic logging tools like Grafana and Datadog, which primarily focus on operational metrics such as latency and token usage. Unlike traditional logging, which merely confirms the completion of requests, Braintrust evaluates responses against quality standards, identifying failures in the execution chain and providing a structured path for resolving production issues. This approach allows developers to detect and verify improvements before these reach end-users, ensuring higher accuracy, relevance, and safety of AI outputs. Braintrust integrates evaluation directly into production workflows, offering automated scoring, prompt versioning, and CI quality gates to maintain high standards and prevent regressions. It maps every execution step, from retrieval to final output, enabling detailed root-cause analysis and prompt lifecycle management, which ensures that quality improvements are consistently enforced. This comprehensive governance layer allows organizations like Notion and Stripe to manage AI systems effectively, using Braintrust to align evaluation results with deployment decisions, thereby ensuring output correctness in line with business requirements.

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