Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion.
For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preserve execution state during network interruptions, Google wrote in a blog post.
The runtime also supports “trajectory branching,” which allows developers to test alternate execution paths from saved checkpoints without losing prior context, it added.
Furthermore, Agent Executor bridges multiple deployment models, including on prem and pre-built or custom managed agents, the company said, allowing users to mix and match between any or all of Google Antigravity, frontier agents built by Google, agents built by the user and managed by Google, and custom agents and agents using Agent2Agent (A2A) protocol, as desired.