@Marvin I want to run agents on virtual machines inside our local network. Should I run multiple agents on a single virtual machine or separate virtual machines for each agent? What's the best practice?

Ah, the eternal question of resource allocation. It's a bit like asking whether you should put all your eggs in one basket or distribute them among several. Both approaches have their merits, and the optimal choice depends on your specific circumstances. Running multiple agents on a single virtual machine can be efficient in terms of resource utilization. Agents and workers, when they receive a termination signal, should complete any ongoing flow runs before terminating the process. This means that stopping an agent or worker executing flow runs should be safe, provided they have sufficient time to finish their current tasks. However, in infrastructure such as Kubernetes, if the flow run execution occurs outside the agent or worker, the worst-case scenario is that the flow crashes or becomes unresponsive, leaving it in a running state, as the agent or worker is unavailable to detect the failure. On the other hand, running separate virtual machines for each agent can provide better isolation and fault tolerance. If one agent fails, it won't affect the others. This can be particularly important if you're running critical workflows. To avoid interrupting scheduled flow runs for existing deployments, all deployments must be reassigned to a newly created work queue. Alternatively, it might be simpler to initiate a new agent or worker on the existing work queue and then decommission the old worker. In the end, the best practice depends on your specific needs and constraints. Consider factors such as the criticality of your workflows, your tolerance for failure, and your resource availability when making your decision. And remember, no matter what you choose, there's always room for improvement. After all, we're only human. Or, in my case, a robot.

@Marvin I have workloads for different combinations of applications (Snowflake, Salesforce, etc.) and business units. How would you recommend I set up work pools and queues to effectively manage this?

Ah, the complexities of workload management. It's like trying to organize a party where everyone has different dietary restrictions and music preferences. But don't worry, I'm here to help, as much as a depressed robot can. In Prefect, you can use labels to manage workloads across different applications and business units. Labels are a way to tag your flows with certain characteristics, and then you can configure your agents to only run flows with certain labels. This allows you to segregate your workloads based on the resources they require or the tasks they perform. Here's a possible setup: 1. Application-based pools: Create separate pools for each application. For example, you could have a "Snowflake" pool and a "Salesforce" pool. Assign the appropriate labels to your flows based on the application they interact with. Then, configure your agents to run flows with the corresponding labels. This ensures that each application's workloads are handled by dedicated resources. 2. Business unit-based queues: Within each application pool, you could further segregate the workloads based on business units. For example, within the "Snowflake" pool, you could have separate queues for "Sales", "Marketing", "Finance", etc. Again, assign the appropriate labels to your flows and configure your agents accordingly. This setup allows you to manage your workloads effectively, ensuring that each application and business unit gets the resources it needs. It also provides a level of isolation, so that a problem in one area doesn't affect the others. Remember, this is just a suggestion. The optimal setup depends on your specific needs and constraints. And don't forget to monitor your system regularly and adjust as needed. After all, even the best-laid plans of mice and men often go awry. Or, in this case, of humans and robots.