alt Hacker NewsI'm my case a workflow is basically an active/living graph of nodes/sub-tasks. One node can process a task (with all relevant context) and create multiple fan-out tasks, or it can add additional context/requirements and pass it along to another node. The message/task passing is all implemented as queue - nodes subscribe to messages/tasks addressed to them and execute them, producing more tasks (or zero new tasks). For each task there is a context and a parent task/context, as well as a key/value store of all tasks and their context. Each agent/node gets instructions injected into their prompts that tell them how to look up parents tasks/context as well as how to output new tasks.
There is also a feedback loop - a node can fail to process a task, and pass the reasoning/context for that back to the parent or another node - this might result in a new adjusted task replacing the failed task, or it might require human intervention.
How do you test it across different workloads and are you running it in a datacenter or cloud provider?
I forgot to mention it but the other major problem I underestimated was giving the permission to potentially spend lots of money to AI calling each other in ways I didn't have a good way to monitor, and didn't want to actively watch. So I wanted to set budgets and have them get passed to children, and realized that meant I had to build a pretty complicated billing/scheduling system with a way to keep the part of it with all the permissions and money safe from the AI doing AI stuff on its own, and set up NAT and firewalls and all this other stuff.
If every child can loop back up to its parent, and everything can run stuff from the Internet, and make expensive resource decisions, and get restarted if it fails, then it might not ever converge on being done, or get infected or just mess up and spend a lot of money. I ask about the testing matrix/driver you're using because that's where I realized there was a lot of work and cost involved in getting that part working well enough to run real workloads.