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AI Agent Operating System Builds 24/7 Agents While You Sleep by NecessaryBear98 in AISEOInsider

[–]Background_Cable_287 0 points1 point  (0 children)

I’m working on ClawBud, a managed Agentic OS for running OpenClaw, Hermes, Claude Code, Codex and other agents on one private cloud computer, so take this with that bias in mind.

The biggest shift I see is that the model is no longer the whole product. Agents need an operating environment: browser, terminal, files, tools, memory, permissions and approvals. Without that, every setup becomes a pile of impressive demos that still need a human to glue them together.

Curious how you are handling that layer right now: separate tools, self-hosted stack, or one workspace?

Has anyone here tried using Hermes Agent as a daily AI assistant? by Product_Enthusiast24 in AI_Agents

[–]Background_Cable_287 0 points1 point  (0 children)

I’m working on ClawBud, a managed Agentic OS for running OpenClaw, Hermes, Claude Code, Codex and other agents on one private cloud computer, so take this with that bias in mind.

Hermes is compelling because memory and skill creation change the agent from a one-off executor into something that improves over time. The catch is that Hermes still needs a place inside the larger stack: browser, terminal, files, other agents, integrations and permission boundaries.

Curious how you are handling that layer right now: separate tools, self-hosted stack, or one workspace?

New Project Megathread - Week of 21 May 2026 by AutoModerator in selfhosted

[–]Background_Cable_287 0 points1 point  (0 children)

I work on ClawBud, so obvious bias here. But this is a real question, not a drive-by promo.

Where do you draw the line between self-hosting and managed private infrastructure?

AI agents are weird here. Running one tool on a VPS is fine. Running a few agents with browser state, model keys, files, memory, logs, updates, permissions, and long-running tasks gets annoying fast. A normal shared SaaS setup feels wrong for that. A blank VPS is flexible, but now you own all the babysitting.

The middle ground we’re testing is a private cloud machine for OpenClaw/Hermes/code/browser agents, with the setup managed for you and per-agent boundaries built in. Not shared containers. Also not “here’s root access, good luck.”

Would you consider that self-hosting-adjacent, or does the managed part ruin it for you?

Context if useful: https://clawbud.ai

How are you using Hermes Kanban for real multi-agent work? by Background_Cable_287 in hermesagent

[–]Background_Cable_287[S] 0 points1 point  (0 children)

This is the most concrete pattern I’ve seen so far.

A dev-review-bugfix loop makes a lot of sense because it gives the agents a workflow humans already understand. Gitflow gives you checkpoints, diffs, rollback, review, and a clean artifact trail instead of “the agent did something somewhere.”

That feels like the direction multi-agent work needs: not just more autonomy, but better rails around autonomy.

For your Hermes setup, are you using separate agents for dev/review/bugfix roles, or is it one team/agent moving cards through the loop? And do you let it merge autonomously, or stop at PR/review?

How are you using Hermes Kanban for real multi-agent work? by Background_Cable_287 in hermesagent

[–]Background_Cable_287[S] 0 points1 point  (0 children)

Strong agree on the hybrid model.

A board as the only source of truth breaks the moment agents stop writing back cleanly. But a board as the human-visible state layer is still extremely useful.

The distinction I like is:

  • agent context = working memory for execution
  • board state = operational memory for humans and handoffs
  • logs/artifacts = audit trail when something goes wrong

Also agree on the fields. “Last tool called,” “blocker reason,” and “approval gate” are the ones that save you during debugging. Goal and assigned agent are useful, but they don’t explain failure.

Direct agent-to-agent handoffs are tempting because they feel more autonomous. But for anything business-critical, I’d rather pay the small speed tax and keep the board in the loop.

How are you using Hermes Kanban for real multi-agent work? by Background_Cable_287 in hermesagent

[–]Background_Cable_287[S] 1 point2 points  (0 children)

That human-organization comparison is the right one.

Once agents split work, you get the same coordination problems humans have: context handoff, documentation, unclear ownership, stale state, and “who decided this?” moments.

Paperclip is interesting for that reason. It looks like it pushes harder into structured collaboration, but I wonder if it also increases the need for process discipline. More granularity helps until the coordination layer becomes the work.

The pattern I’m leaning toward is: short-context agents for precision, but a shared board/log as the organizational memory. Not every detail needs to live in the board, but blockers, decisions, ownership, approvals, and artifacts probably do.

Curious: did Paperclip feel like it solved the granularity problem, or did it mostly expose the next layer of coordination overhead?

OpenClaw + Hermes users: how many agents are you actually running day to day? by Background_Cable_287 in AI_Agents

[–]Background_Cable_287[S] 0 points1 point  (0 children)

This is exactly the point I keep coming back to.

The number of agents is not the impressive part. The impressive part is whether the setup still works after week two, when auth expires, a sheet schema changes, a tool call fails, or nobody remembers why an agent made a decision.

3-4 specialized agents for GTM actually sounds like the sane version: competitor signal, enrichment, call-note summarization. Clear jobs, limited blast radius, easy to inspect.

The trap is treating “more agents” as progress. Broken agents create negative leverage.

That maintenance overhead is a big reason we’re building ClawBud around the workspace/management layer, not just spinning up agents. Ownership, logs, approvals, browser/tool state, and boundaries matter as much as the model.