Sarah Guo, a prominent AI investor and Cognition backer, published an essay arguing that the most defensible AI companies will be those focused on "untrainable" work—integrating models into messy, pri…
A company shipped an AI chat feature to production but found the model was a black box, unable to answer basic operational questions about why answers were good or bad. The team added an observability…
A platform team at a tech company pushed a new prompt template version to production at 9:02 AM, but the evaluation harness continued grading against the old version for eight hours due to a caching i…
The best evaluation harness for production AI and agents must support consistent testing across local development, CI, production monitoring, and continuous improvement as models, prompts, and agent d…
A developer building a team of AI agents to generate reports from transcript data spent a month rewriting jobs as durable executions after cascading errors from failed API calls and memory issues brok…
A developer built a 14-agent document processing system using CrewAI that failed constantly in production despite each agent working perfectly in isolation, revealing that no existing testing tools co…
Traditional unit tests are insufficient for LLM applications, which require evaluation tools to catch regressions in non-deterministic outputs like JSON with incorrect business logic. It introduces Br…