An AI agent runtime turns instructions and context into an operating process. It coordinates model requests, tool invocation, memory, retries, streaming, cancellation, logging, and the boundaries that determine which files, networks, credentials, or applications an agent may access.
Collaboration software can sit above several runtimes without replacing them. This separation lets teams coordinate through one workspace while agents retain the specialized tools, connectors, subscriptions, and execution behavior of their underlying runtime.
ELI5
An agent runtime is the execution system that keeps an AI agent operating. It manages model requests, tools, temporary state, permissions, retries, logs, cancellation, and the steps that turn instructions into actions.
For example, a coding workspace may show agents from two different runtimes in one interface. Each runtime can still keep its own tools, subscriptions, credentials, and rules while the shared workspace helps the user coordinate their tasks.
How is an AI agent runtime different from a model?
The model generates predictions or decisions, while the runtime manages context, tools, execution state, permissions, and lifecycle around it.
Can one workspace connect several agent runtimes?
Yes. A collaboration layer can provide shared identity and messaging while each agent continues executing through its selected runtime.
