Test-Driven Generation: How SKYCOT Achieves 96% Correctness

The standard approach to AI code generation is simple: generate code, run it, fix errors, repeat. This generate-then-test loop typically achieves around 60% correctness on the first pass. Each fix cycle costs tokens and time, and errors can cascade across files.

SKYCOT flips this process using the AgentCoder pattern from recent AI research. For each build session, an independent test agent writes comprehensive tests before any implementation code exists. These tests define the contract: what functions must exist, what they should return, and what edge cases they need to handle.

A separate code agent then implements the functionality to pass those tests. Because the tests are written by a different agent with no implementation bias, they catch assumptions and edge cases that generate-then-test misses. The result is production-quality output on first generation — compared to roughly 60% for the traditional approach.

This matters because correctness compounds. In a typical SKYCOT build with 10-15 sessions running in parallel, the difference between 60% and production-quality per-session output is the difference between 0.6% and 54% overall build success rate. Test-driven generation makes parallel orchestration viable.

The testing approach also gives you confidence in the generated code. Every generated app ships with tests that document the expected behavior. When you modify the code later, those tests catch regressions. It's not just about generating working code — it's about generating maintainable code.

Token cost is a concern with two-pass generation, but the savings from avoiding error-fix loops more than compensate. Our data shows test-driven generation uses 15-20% more tokens per session but eliminates an average of 2.3 fix cycles that would each cost 30-50% of the original generation cost.