Agent Runtime

An agent runtime is the execution environment where AI agents plan work, call tools, read data, store intermediate state, and interact with external systems. It is more than a wrapper around a language model. A runtime usually includes identity, permissions, tool registration, memory and context handling, execution policies, error handling, logging, observability, and sometimes handoff mechanisms between agents. In prototypes, this logic often lives inside scripts, prompt chains, or ad hoc automation. In production systems, the runtime becomes the operating layer that decides what an agent is allowed to do, how long a task may run, what it costs, and how outputs are checked. That makes agents more reproducible, safer, and easier to audit. The concept matters because many enterprise agent projects do not fail because the model is weak; they fail because the surrounding runtime is missing. Without a proper runtime, there are no reliable tool boundaries, no durable logs, no consistent recovery behavior, and no clear accountability when an agent makes a bad decision.