A benchmark based evaluation of how deep codebase context improves coding agent success on large, complex, real world codebases.
Large repositories, multi-file changes, and long-horizon tasks see the biggest lift—where agents must reason across dependencies, not just edit isolated code.
As tasks span more files, standalone models drop off sharply, while AI Architect continues to resolve complex changes.
Time + cost benefits increase large, multi-file tasks.
With Bito’s AI Architect
Claude Opus 4.6 (baseline)
Models generate code. Systems require reasoning.
AI Architect builds a knowledge graph from your code, commits, issues, docs, and past decisions, then delivers deep system context across your engineering workflow, including grounded coding via MCP.
No code storage or model training. End-to-end data encryption. Enterprise-ready.
*Note: This evaluation was conducted by The Context Lab, an independent 3rd party that performs agent evaluations in a tightly controlled measurement environment.