Temporal remembers

MLOps.community · Advanced ·🤖 AI Agents & Automation ·3mo ago

Key Takeaways

Temporal Technologies' approach to reliable infrastructure for production AI systems and long-running agent workflows, featuring automatic recovery from failures and seamless code version updates

Full Transcript

You kick that job off, it runs for a week, and now it's broken. And because of something silly, right? And now you're like, "Okay, well, I kind of have the pieces. I can pull them together, and I can write a new program that will pick up kind of from where that one broke, and like I'll do that." But it's what Temporal you don't do that. You just like fix the thing, and then the thing finishes. >> It remembers where it broke down. >> It remembers where it broke. Yes, it can move to the new version of the code. Uh-huh.

Original Description

Johann Schleier-Smith is the Technical Lead for AI at Temporal Technologies, working on reliable infrastructure for production AI systems and long-running agent workflows.
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Temporal Technologies' approach to reliable infrastructure for production AI systems enables automatic recovery from failures and seamless code version updates, ensuring uninterrupted workflow execution. This is crucial for long-running agent workflows that require high reliability and minimal downtime. By leveraging Temporal's technology, developers can focus on building and improving their AI systems rather than manually recovering from failures.

Key Takeaways
  1. Identify potential failure points in AI workflows
  2. Implement automatic recovery mechanisms using Temporal
  3. Update code versions seamlessly without interrupting workflow execution
  4. Monitor and maintain workflow performance
  5. Optimize workflow execution for reliability and efficiency
💡 Temporal's ability to remember where a workflow broke down and automatically recover from failures enables seamless and reliable execution of long-running agent workflows

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