Context windows are a fundamental constraint in the development of AI agents that manage multi-step workflows, determining their ability to function effectively under real-world conditions. These windows represent the working memory of large language models (LLMs), akin to human short-term memory, where the amount of information retained is limited by a token count. When context windows fill up, earlier information is lost, leading to incomplete data processing and potentially incorrect results without any explicit error notification. This issue is particularly pronounced in agentic workflows, where context and token usage rapidly accumulate across multiple LLM calls. Effective management of context involves strategies such as context engineering, which includes compressing tool outputs, summarizing intermediate results, and prioritizing critical information to stay within token budgets. Observability tools, such as Opik, are essential for tracking token usage, monitoring context limits, and identifying where information is dropped or compressed, thereby preventing context-related failures and optimizing performance and costs in LLM applications.