This article discusses the importance of thorough evaluation when deploying AI agents. It outlines how AI development differs from traditional software, identifies three essential evaluation components, and provides a practical five-step process for effective assessments.

This article discusses a framework for measuring how well different compression methods preserve context in AI agent sessions. It compares three approaches, finding that structured summarization from Factory maintains more critical information than methods from OpenAI and Anthropic. The evaluation highlights the importance of context retention for effective task completion in software development.