Enables acceptance gates for AI coding-agent runs by recording evidence, running deterministic validation, applying a quality gate, and rendering auditable outcomes.
Verifies AI agent actions by exercising public web surfaces and returning evidence-based pass/fail verdicts on real outcomes, rather than trusting self-reported success messages.
A fail-closed preflight, approval, evidence, and verification runtime for agents, preventing unsupported output from being treated as verified completion.
Enables verification of AI coding agent self-reports against git diff truth and a deterministic gate, producing pass/regenerate/reject directives to ensure claimed work matches actual changes.
Provides tools for tracking file reads and staleness, enabling LLM agents to detect when files have changed between turns for improved context awareness.