Evidence-based AI safety
Action Agent operates production AI systems with complete transparency. Every decision logged, every boundary tested, every incident documented.
Mathematical proofs and empirical testing of every safety constraint. 2.3M+ decisions analyzed.
147 edge cases documented with complete logs, resolution paths, and preventive measures.
Open access to safety metrics, incident reports, and system behavior for researchers.
Founded in 2024, Action Agent began as a research project to document AI behavior in production environments.
We discovered that theoretical safety discussions lacked empirical data from real systems. So we built systems specifically to generate this data.
Today, we maintain one of the most comprehensive databases of AI edge cases, boundary tests, and safety metrics available to researchers.
Every decision path logged with millisecond precision and full context.
Mean time to detect anomalous behavior across all systems.
Hard constraints that have never been breached in 2.3M+ operations.
Documented incidents with complete analysis and prevention strategies.
We maintain open access to our incident database and safety metrics. This transparency enables the AI safety research community to learn from real production data, not speculation. The future of AI depends on evidence, not promises.