Vocabulary for building AI agents that plan, act, and recover from failure in production systems.
Tying a model's claims to a verifiable source, like a document or a tool result, instead of letting it answer purely from its own trained knowledge.
Constraints put around an agent's behavior to prevent harmful, out-of-scope, or unintended actions. Can be implemented as input/output filters, tool restrictions, or a separate validator model.
A model confidently generating information that sounds plausible but is fabricated or wrong. In agentic systems this is especially dangerous because the agent may act on false information.
A design pattern where the agent pauses at defined checkpoints and waits for a human to review or approve before continuing — rather than running fully autonomously.
A property of an action where running it twice with the same input has the same effect as running it once, so a retry after a timeout can't cause double damage.
Logic that notices when an agent is repeating the same action or getting the same result over and over, so the system can break out instead of burning budget indefinitely.
A pass where an agent reviews its own previous output or action result and decides whether to revise it, retry it, or move on.
Code that automatically re-attempts a failed action a limited number of times, usually with a growing delay between tries, before giving up.
The rule that tells an agent when to stop looping and return a final answer, whether that's reaching the goal, hitting a step limit, or running out of budget.