The field of research and practice focused on ensuring AI systems behave safely, reliably, and in alignment with human values. For agentic systems, AI safety encompasses preventing harmful actions, maintaining human oversight, ensuring predictable behavior, implementing kill switches, and designing systems that fail gracefully — particularly critical as agents gain more autonomy and access to real-world tools.
Related Terms
Guardrails
Safety mechanisms and constraints built into AI systems to ensure they operate within defined boundaries. Guardrails include input validation, output filtering, content moderation, action restrictions, budget limits, and human approval gates. They are essential for deploying autonomous agents in production — preventing harmful outputs, unauthorized actions, and runaway costs.
Human-in-the-Loop (HITL)
A design pattern where AI systems include checkpoints for human review, approval, or intervention at critical decision points. HITL ensures that autonomous agents operate safely by keeping humans involved for high-stakes decisions while letting AI handle routine work independently.
AI Hallucination
A phenomenon where an AI model generates content that is factually incorrect, fabricated, or unsupported by its training data or provided context. Hallucinations are a key challenge in deploying AI agents for production use — addressed through techniques like RAG (grounding responses in real data), human-in-the-loop review, confidence scoring, and output validation against authoritative sources.