Human-in-the-loop is a design approach where people review, correct or approve an AI system's outputs or actions at chosen points. It keeps human judgement in decisions that are risky, ambiguous or hard to reverse.
Agentic AI is a way of designing systems in which AI pursues a goal with some autonomy. It plans steps, calls tools and acts on results, often through several coordinated agents working inside limits people set.
Guardrails are checks placed around a language model that inspect what goes in and what comes out, blocking or correcting responses that break rules. They enforce policy in code rather than trusting the prompt.
RLHF is a training method that uses people's preferences between model outputs to shape a model's behaviour. It is mainly used to make language models more helpful, honest and safe.
An AI agent is software that wraps a language model in a loop with tools and memory so it can work toward a goal. It chooses an action, reads the result and continues until the task is done.
AI governance is the set of policies, roles and controls an organisation uses to decide how AI systems are built, approved, monitored and retired. It is how responsible AI principles get enforced.
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