The text discusses the Agentic Evaluation Framework provided by Deepchecks, which is designed to enhance the observability and evaluation of complex agentic systems using tools like CrewAI. This framework addresses the challenges posed by modern applications that autonomously think and act through multi-step processes, making traditional debugging methods obsolete. It operates on three core pillars: automatic trace logging and visualization, built-in agent span properties, and cross-span data access for enriched evaluation. The text provides a detailed guide on setting up a multi-agent system using Deepchecks, explaining the installation of necessary dependencies, environment configuration, and API key management. It covers the creation and execution of a CrewAI project, which involves logging agent traces, visualizing performance, and comparing different model versions to optimize intelligent pipelines. Deepchecks offers a comprehensive view of agent performance by analyzing metrics such as Plan Efficiency and Tool Completeness, enabling teams to make informed improvements in agent reasoning and execution.