Epistemic Engine: open-source tool verifies AI code, forecasts failures

Epistemic Engine frames code verification around the concept of beliefs—formal and informal claims the code makes about itself ("this function authenticates the caller", "this value is never null")—and reconstructs their justification from git history. This is a meaningful shift from traditional static analysis: instead of pattern-matching vulnerability signatures, the tool models how well-founded a piece of code is and how that confidence erodes as the codebase evolves. The dual tool design (ee guard for immediate blocking, ee predict-chain for long-term forecasting) addresses two separate use cases: teams reviewing AI-generated code can catch obvious flaws before commit, while maintainers can forecast which subsystems are at risk of collapse and prioritize remediation by cost-benefit.

The tool's honesty-first design—refusing to output a prediction if back-testing shows it doesn't outperform baseline, labeling all cost figures as assumptions, and explicitly reporting when it can't trust its own forecast—reflects a deliberate choice to avoid the temptation to appear confident when uncertainty is real. Field testing on 1,500+ production files with reported precision / recall / false-positive rates of 1.0 / 1.0 / 0.0 (lower bound 0.92 on the validation set) suggests the guard tool has genuine practical accuracy, though the lower bound on a 114-sample corpus is the more conservative metric. The implementation is deterministic and offline throughout, with no LLM or network dependency in any computed value, making results reproducible in CI and suitable for long-term code audit trails.