AI Safety
Definition
The field of research and engineering focused on making AI systems operate safely and beneficially. AI safety encompasses alignment (ensuring AI goals match human intentions), robustness (maintaining performance under adversarial conditions), interpretability (understanding AI decision-making), and governance (controlling what AI can do). The field spans both long-term existential risk research and near-term production safety engineering.
Why It Matters
Near-term AI safety is an engineering problem: AI agents can hallucinate, go rogue, execute destructive actions, and bypass constraints. These failures aren't hypothetical — they happen in production today. Every AI agent with tool-use capabilities is a potential safety incident waiting to happen.
How Exogram Addresses This
Exogram addresses near-term AI safety through deterministic execution governance. Rather than trying to make models "safer" through training (which is probabilistic), Exogram provides the infrastructure layer that blocks unsafe actions regardless of model behavior. Safety as infrastructure, not as training.
Related Terms
Key Takeaways
- → This concept is part of the broader AI governance landscape
- → Production AI requires multiple layers of protection
- → Deterministic enforcement provides zero-error-rate guarantees