An artificial intelligence agent runtime surrounds a model with the capabilities needed to perform work. It manages instructions, tool invocation, credentials, state, timeouts, retries, logging and the boundaries within which the agent can act.
A production runtime must remain reliable when tools or legacy systems respond slowly or fail. It should preserve state, capture evidence and make each decision understandable. Treating the runtime as a distinct system avoids assuming that replacing the model will solve operational failures.
Acronyms and aliases
agent runtime variantAI agent runtime variant
Related terms
Frequently asked questions
What does an AI agent runtime manage?
It can manage model requests, tool access, state, permissions, retries, timeouts, traces and policy checks. The exact responsibilities depend on the agent architecture.
Why should an agent runtime be bounded?
Boundaries limit which systems and actions an agent can reach. This reduces the impact of mistakes and makes testing, approval and recovery more predictable.
