We evaluate multiple AI code understanding agents across a set of curated questions drawn from real-world repositories to highlight practical differences in how they retrieve context, reason across files, and explain code behavior. The goal is to surface how well each agent handles tasks that resemble everyday developer queries.

Each agent is evaluated using default configurations with no additional tuning or repository-specific customization. We measure answer accuracy, reasoning quality, context retrieval, and consistency across queries to reflect how these systems perform in realistic development workflows.