Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Cloud & Infrastructure
Google Cloud Blog·May 20·5 min read
Introducing Agent Executor, Google’s distributed Agent Runtime
TL;DR
Long-running workflows for AI agents are often fragile and difficult to manage in production. Google has developed Agent Executor, an open-source runtime that ensures durable execution, secure isolation, and session consistency for agents.
✦ Why It Matters
Engineers can leverage Agent Executor to build more reliable and secure AI agent workflows in production environments.
Key Takeaways
- Implement durable execution to enhance agent reliability.
- Utilize secure isolation to protect sensitive data.
- Leverage trajectory branching for flexible decision-making.
- Explore deployment options to avoid vendor lock-in.
Full Summary
As AI agents take on more complex tasks that can last hours or days, managing these long-running workflows has become increasingly challenging. Google has introduced Agent Executor, an open-source runtime that provides essential capabilities such as durable execution, which allows agents to resume after interruptions, and secure isolation to protect against malicious activities.
It also features session consistency to prevent data corruption during concurrent updates and connection recovery for improved user experience during disconnections. Additionally, trajectory branching enables agents to explore different decision paths without losing context.
This runtime is designed to work seamlessly with various deployment models, allowing enterprises to maintain control over their workflows and avoid vendor lock-in.