How We Solved the Chaos of Multiple Hermes Workflows
Managing multiple Hermes workflows last week felt like chaos: coding tasks, research pipelines, automations, and background processes all running across projects. The agents weren’t the problem it was keeping everything organized.
After some trial and error, manual tracking proved impossible. That’s when we built Minions, our Mission Control for Hermes workflows.
Here’s what it brings to the table:
Kanban Overview: See all tasks in one place, with clear statuses like “In Progress,” “Ready for Review,” and “Done”.
Autonomous Execution with Human Oversight: Agents handle tasks independently, but nothing is marked complete without approval.
Live Task Streaming: Watch reasoning steps, tool calls, and outputs as they happen.
Per-Task Control: Adjust model behavior and effort for each task.
Local-First Option: Run everything on SQLite locally—no cloud required.
Early users say it cuts 60–70% of the time they used to spend monitoring workflows. Tasks that used to demand constant attention now move forward on their own, freeing the team to focus on higher-value work.
Could autonomous task execution improve your workflow? Which features would matter most to you?
Running a single workflow is usually manageable but once you have multiple agents working across different projects, keeping track of what's happening becomes the real challenge. I'm curious how you handle situations where an agent gets stuck or produces something that needs rework. Do you have a way to catch those issues automatically or does it still require manual review?
Yeah that’s exactly the pain point we ran into. Right now we handle it with a mix of both there’s automatic detection for stuck/low confidence runs (timeouts, no tool progress, repeated loops) and those get flagged for review. But for anything that “looks complete but feels off,” we still rely on human approval in the loop.
We’re slowly moving toward smarter recovery rules where tasks can self retry or re route but keeping humans in control for now has been the safest balance.
That “looks done but isn’t quite right” case is the tricky one. Like that’s where most systems still end up relying on human eyes somewhere in the loop. Curious if you’ve seen it happen more in certain types of tasks vs others?
We’ve noticed it happens way more in open-ended tasks like research summaries or multi step content generation where “correctness” is a bit subjective. Anything with clear rules (like code execution or structured transformations) is usually much cleaner.