My fix for 200 messy AI threads

I love autonomous agents.

I use Hermes. I use OpenClaw. I use Codex. I use Claude Code. On a normal day, I can have 10 to 15 parallel agent threads running at once.

You start one thread for a YouTube packaging idea.

You start another for cutting costs.

You start another to connect APIs, pull Apollo data, or invite people to a 9 to 10 figure AI operator dinner.

Then you go to the gym, hop on the treadmill, dictate a few more ideas, and suddenly you have agent sprawl.

Not anymore.

The hidden cost is duplicated agent work

Most teams think the bottleneck is agent capability.

I think the bigger bottleneck is human context.

The agent can remember the thread. But you can’t remember 15 threads at once. You forget what you started, restart from scratch, duplicate work, and ask the same question over and over:

"Can you tell me in one sentence what we are working on here and what the next step is?"

I was doing that across Slack threads constantly.

Then I would come back after a weekend and see 20 chats from Sunday, 15 from Saturday, 10 from Friday, and before you know it, you are staring at 200 threads.

The harsh reality is that 50% of them might be related. Some are duplicates. Some are stale. Some already solved the problem you just restarted.

That is how AI makes you slower while feeling like it made you faster.

Slack is great for multiplayer, but weak for personal command

A lot of AI people will tell you to run your agents inside Discord.

That is fine if your team works in Discord.

If your people already live in Slack, Slack is the highest leverage place to put agents because it is multiplayer. Multiple people can talk to the same system, share context, and keep work inside the company operating layer.

Telegram has topics. iMessage is fine for quick one-offs. But moving the whole team to another app creates more friction than leverage.

Slack solved the team collaboration problem. It did not fully solve the solo operator problem.

When I am doing deep work, I need sessions, priorities, artifacts, skills, and follow-ups in one place.

That is why the Hermes desktop app clicked for me.

The desktop app turns agent chaos into a workbench

My left sidebar used to be a mess.

A bunch of sessions. No clean order. No obvious priority.

So I asked Hermes to organize my sessions on the left, then put the higher priorities higher.

Now I can see P0 work at the top. CFO spending. HubSpot work. Pre-meeting context. Training work. Follow-ups. Sessions that need attention and sessions that do not.

That sounds simple, but simple is the unlock.

Claude Code and Codex already trained me to expect a project-style workspace. Hermes copied the model for agent sessions, and that was enough to make the system usable at higher volume.

The other underrated piece is skill and artifact visibility.

When skills are buried inside Slack threads, they may as well not exist. In the desktop app, I can search skills, find artifacts, pull up new pages, images, creatives, or old sessions, and reconnect the work faster.

The operator got a better cockpit.

The resolver is where the work starts compounding

The most useful thing I built around this is a resolver.

It looks across my Codex work, Claude Code work, and Hermes work from the day. Then it helps decide what to keep, what to consolidate, what to delete, and what should move higher in priority.

That is the layer most companies are missing.

They keep launching new agents before they have a cleanup loop.

If you do not have a resolver, every new agent adds another stream of unfinished work. If you do have one, the system starts to compound because the best work survives and stale work stops clogging the queue.

This is also how we think about Single Brain.

Your agents should connect to your company brain through Slack or Microsoft Teams, pull from systems like Gong, HubSpot, and marketing analytics, then run workflows that get better every week.

Maybe that is an ad creative agent.

Maybe it is an outbound agent.

Maybe it is a pre-meeting context agent that checks your calendar and connected systems before the call so you can ask better questions and move the deal forward.

The point is managed revenue agents that compound with your business.

The operator playbook is priority, memory, cleanup

If you are working on three or more AI projects right now, here is the practical playbook.

First, keep your team agents where the team already works. For most companies, that means Slack or Microsoft Teams.

Second, use a desktop command center for solo work. You need pinned sessions, priorities, artifacts, skill search, and follow-up visibility.

Third, create a resolver. Have the system review what happened across your agents and tell you what to keep, merge, delete, or prioritize tomorrow.

Fourth, stop measuring agent progress by how many threads you launched. Measure it by how much less work you redo.

That is the real AI productivity test.

If your agents create more context for you to manage, you just built another inbox.

If your agents preserve memory, surface priorities, and consolidate duplicate work, you built leverage.

Watch the full breakdown here: https://www.youtube.com/watch?v=QzpGmx5p5aE

To building agents that compound instead of clutter,

Eric Siu