Why AI-Generated Code Is Easy but Engineering Trust Is Hard

Summary

At Salesforce, engineering teams discovered that AI coding agents could produce internally consistent code and passing test suites that still failed to meet actual requirements. To ensure correctness before writing code, the team developed an agentic Spec-Driven Development workflow centered on explicit specifications and gated verification. The system separates evidence-based repository lookups handled by agents from subjective judgment calls escalated to human engineers. Implementation plans must cite repository evidence reviewed by a Skeptic Agent, while a Compliance Matrix traces each success criterion directly to executable test proof. Finally, a multi-agent review system evaluates the implementation under an asymmetric rule allowing an independent judge to downgrade passing checks but never override failed gates.

Takeaways

1. 1

   Uncertainty is split into lookup and judgment: agents resolve lookup questions by finding evidence in repository history, code, and tests, while humans handle judgment decisions that cannot be recovered from evidence.

2. 2

   A Compliance Matrix maps every specification success criterion to executable test and implementation evidence, ensuring tests validate requirements rather than just self-consistent agent code.

3. 3

   The Conclave multi-agent review process uses an asymmetric evaluation rule where an independent Judge Agent can downgrade passing results based on critic findings, but cannot upgrade a failed gate.