Action Space Decision Policy is a critical framework that determines how an agent selects and executes actions from a set of possible options, known as the action space.
Adversarial Robustness refers to the ability of an artificial intelligence (AI) or machine learning (ML) system to maintain reliable performance when faced with adversarial inputs.
Agent Alignment refers to the process of ensuring that an autonomous or semi-autonomous AI agent consistently acts in accordance with intended human goals, values, constraints, and expectations throughout its operation.
Agent Competition refers to the dynamic interaction among multiple agents in an artificial environment, where each agent seeks to achieve specific goals, objectives, or advantages, often at the expense of others.
An agent controller is the control layer in an agentic AI system that manages the agent’s overall behavior across a task. It decides how the system moves from goal intake to planning, tool use, verification, and final delivery.
Agent Evaluation Metrics are a structured set of quantitative and qualitative measurements used to assess the performance, reliability, safety, and effectiveness of agentic AI systems.
An agent executor is a specialized component or role within an agentic AI system responsible for carrying out concrete actions determined by a reasoning or planning process.
