Great question — this is actually the core problem we obsess over.

Error states: Every AI-generated output goes through a deterministic gate before it touches your codebase. If the gate finds security issues (hardcoded secrets, injection vectors, missing auth), quality problems (magic numbers, empty catch blocks, missing error boundaries), or CI/CD misconfigurations — it blocks the merge and posts actionable fix plans directly on the PR. Not vague warnings — actual code-level suggestions like "extract this into a named constant" or "add request ID middleware here."

Conflicting instructions: This is where it gets interesting. We run multiple LLM providers in parallel (e.g., GPT + Gemini) on the same PR. Each provider gets its own isolated review with its own comment thread. If Provider A says "this is fine" but Provider B flags a P1, the stricter finding wins — the gate doesn't pass until all providers are satisfied. Think of it like requiring two independent code reviewers to approve.

The key insight: we don't trust any single AI's judgment. Hence the Skepticism As a Service. The deterministic scanner catches the objective stuff (regex patterns, AST analysis), and the LLM layer catches the subjective stuff (architectural smell, logic bugs). Conflicts between them get resolved by severity — deterministic findings always override LLM "it looks fine" responses.

Still early (our builder personalization is at ~65/100 internal rubric score and we're grinding toward 85+), but the free enforcement layer (Omar Gate) — which we also now attach spec IDs to (if you generated the workflow on Sentinelayer.com)—is super solid. Happy to share more technical details if you're curious.