Agent Failure Recovery refers to the set of mechanisms and processes that enable an agentic AI system to detect failures, respond safely, restore functionality, and resume operation with minimal disruption.
Agent Lifecycle Management is the structured process of designing, deploying, operating, monitoring, updating, and retiring agentic AI systems throughout their operational lifecycles.
Agent negotiation is the structured process by which autonomous or semi-autonomous AI agents communicate, evaluate options, and reach agreements when their goals, constraints, resources, or preferences differ.
An agent runtime is the execution environment and control infrastructure that allows an agentic AI system to run continuously, manage state, invoke tools, and progress through tasks.
Agent Simulation refers to the use of controlled, synthetic, or sandboxed environments to test, evaluate, and refine the behavior of agentic AI systems before or during real-world deployment.
