Databricks announced that its benchmarking study of coding agents on a multi‑billion‑line codebase showed that the Pi minimal harness achieved higher pass rates at lower cost than more complex alternatives. The study examined how the interface through which a language model is invoked affects both expense and quality, using tasks regularly performed by Databricks engineers. Pi ships with only four built‑in tools and a system prompt of fewer than 1,000 tokens, allowing users to add custom tools as needed. When the same model was run with different harnesses, cost per task varied by more than twofold while task quality remained unchanged. With the Opus 4.8 model, Pi achieved the highest overall pass rate and a cost advantage over Claude Code and Codex. Shopify’s engineering team built an Autoresearch extension directly on Pi, reporting unit‑test execution speeds 300 times faster, React component mounting 20% faster, reduced build times across projects and improved pnpm performance. Pi’s limited context window and tighter prompt handling help local models operate within constrained token limits, preserving prompt stability and reducing re‑prefilling overhead. The findings suggest that simplicity and careful context management can yield cost savings and performance gains, and that extensible minimal frameworks may become a preferred foundation for future AI coding tools.