AI Agent Architecture

7 Hermes Agent Use Cases That Actually Work

The interesting Hermes Agent question is not "Can it make a report?" It can. The useful question is: can Hermes turn research, reminders, computer control, and repeated work into something that actually moves the business forward?

Andrew Warner's conversation with Eric Siu is useful because it has both excitement and friction. Andrew shows real Hermes examples. Eric keeps pushing the same practical test: a report is not enough if it does not become a workflow, a task, a draft, a handoff, or a reusable skill.

Video source: Andrew Warner with Eric Siu. Sponsor material is intentionally not included in this post.

JQ AI SYSTEMS take

Hermes gets useful when the workflow ends somewhere operational: a Linear issue, a Notion task, a Slack thread, a saved skill, a draft for approval, or a cron that someone actually reads.


Source note

This post credits Andrew Warner's Hermes/OpenClaw use-case session with Eric Siu and the companion deck at hermes-openclaw-use-cases.reaction.codeshiftagent.com. I used the supplied transcript as source material and avoided embedding sponsor content.

I also checked the official Hermes Agent page from Nous Research, which describes Hermes as an open-source MIT-licensed desktop agent with persistent memory, scheduling, subagents, web/browser tooling, and local, Docker, SSH, Singularity, and Modal backends. I also checked OpenClaw, OpenClaw on GitHub, Linear's developer docs, and Tailscale SSH for the project-management and remote-device parts of the discussion.


The main point

The episode has a repeated pattern:

  • A flashy demo appears.
  • Andrew asks whether it is useful.
  • Eric separates "cool once" from "useful every week."
  • The practical version usually involves a loop, a task system, a memory layer, or a skill.

That is the right lens for Hermes. A personal AI agent is not valuable because it can create more artifacts. It is valuable when it reduces open loops.


7 use cases that actually work

Here is the JQ AI SYSTEMS ranking of the Hermes use cases from the video, ordered by how likely they are to become durable workflows for builders and operators.


1. Control the computer

The opening demo is Hermes controlling a computer, opening VS Code, and typing into Claude Code. That is exciting because it turns Hermes into a coordinator across other agent tools. It can tell Claude Code, Codex, or browser tools what to do while you are not sitting there.

The useful version is not "let Hermes do anything on your machine." The useful version is narrower:

  • Give Hermes a bounded computer task.
  • Use a dedicated workspace or secondary machine where possible.
  • Limit credentials and destructive permissions.
  • Ask it to report progress in a channel you watch.
  • Require approval before publishing, sending, deleting, buying, or changing production data.

This is where desktop agents become interesting: not as a replacement for every app, but as a bridge between apps that do not yet have clean APIs.


2. Competitor research that becomes a task

The competitor-research demo asks Hermes to open a browser, inspect a competitor site, describe the stack, identify features, and save a markdown breakdown.

Eric's critique is the important part: by itself, that is homework. It becomes useful when the output moves into execution:

  • Create a Notion or Linear task from the findings.
  • Summarize only the features worth emulating.
  • Attach screenshots, links, and implementation notes.
  • Ask the coding agent to build a specific small piece.
  • Repeat weekly or monthly as a competitor-watch routine.

A one-off report is content. A competitor-watch skill is leverage.


3. Personal wiki and agent memory

The video spends real time on memory: daily logs, personal wiki pages, Obsidian, QMD, GBrain, GStack, and the idea that Hermes needs better context to stop feeling forgetful.

This maps directly to a business problem. If an agent has to rediscover your projects every session, it is not an operating system. It is a chat window with nostalgia.

The practical memory stack can be simple:

  • Daily logs: what happened, what changed, what decisions were made.
  • Project pages: goals, constraints, owners, current status, open questions.
  • Decision records: why a direction was chosen.
  • Reusable skills: workflows that should not be rewritten every week.
  • Review notes: what the human corrected last time.

Hermes is stronger when memory is not just chat history. It needs structured knowledge it can retrieve and act on.


4. Project handoff through Linear, Notion, or another task system

Eric uses Linear for project tracking because the API is clean and the workflow fits product-building teams. Andrew uses Notion as a task board and describes passing conclusions from Claude desktop into Hermes by creating a Notion task for Hermes to take action on.

The exact tool matters less than the rule: agent work needs a durable place to land.

A useful handoff looks like:

  • Source: Claude, Hermes, Codex, YouTube research, Slack, or a meeting.
  • Destination: Linear, Notion, Trello, GitHub issue, or a project doc.
  • Fields: task title, why it matters, source links, acceptance criteria, owner, due date, risk level.
  • Review: human approves before the task becomes active or public.

Without this handoff, agent output turns into a pile of half-useful documents.


5. Agent channels: Slack, Discord, Telegram, or iMessage

The episode compares Slack, Discord, Telegram, and the newer Hermes desktop experience. The cleanest takeaway:

  • Solo builder: Discord can work well as a personal agent HQ with channels by project or workflow.
  • Team: Slack is usually better because the team already works there.
  • Quick private commands: Telegram or iMessage can be convenient.
  • Serious session management: the Hermes desktop app helps because sessions, profiles, and skills are easier to see.

The deeper lesson is not "Slack versus Discord." It is: do not create an agent surface your team will ignore.


6. Content workflows: videos, comments, lead magnets, and research

The transcript covers Hermes making videos with Hyperframes, clipping workflows with Overlap, daily AI briefings, YouTube comment monitoring, and lead magnets triggered by comments.

These can be useful, but only when the workflow has a clear next step:

  • YouTube research should draft titles, gaps, hooks, or outlines.
  • Comment monitoring should surface the highest-value replies, not spam every viewer.
  • Lead magnets should create a reviewable asset and route it through the distribution tool.
  • Video generation should help with supporting assets, not replace editorial judgment.
  • Daily briefings should be filtered enough that the human actually reads them.

The anti-pattern is a cron that posts a giant daily brief no one opens. A useful cron should be short, ranked, and connected to a decision.


7. Skillify repeated work

The final strong use case is saving a repeated workflow as a skill. In the transcript, the example is turning YouTube competitor research into a reusable competitor-watch skill.

Eric's nuance is useful: you will not always know what should become a skill while you are doing it. A better system watches what you repeat, scores the leverage, and asks whether to skillify the workflow later.

A good Hermes skill should answer:

  • When should this skill be used?
  • What inputs does it need?
  • Which tools can it use?
  • Where should the output go?
  • What does good output look like?
  • What should require human approval?

If you do something three times and it needs judgment, context, and tools, it is probably a skill candidate.


The companion deck includes several supporting Hermes and OpenClaw videos. I am embedding the useful ones here and intentionally leaving sponsor content out.


Builder checklist

Before you add Hermes to your work, write this down:

  1. Workflow: what repeated work should Hermes own?
  2. End state: does it end in a task, draft, ticket, saved file, or review queue?
  3. Memory: what does Hermes need to know about you, your business, and prior decisions?
  4. Tools: which systems does it need, and can it start read-only?
  5. Channel: where will it report progress so you actually see it?
  6. Approval: what actions require human review?
  7. Skill trigger: when should this become a reusable skill?
  8. Stop rule: when should Hermes stop, ask, or escalate?
CTA

Do not use Hermes to create more reports. Use it to close loops: research, decide, draft, route, review, and save the workflow as a skill when it repeats.


Sources

The short version: Hermes is most useful when you stop asking it for isolated demos and start giving it repeatable operating loops. The goal is not more AI output. The goal is fewer dropped threads.

Common questions

What is the best Hermes Agent use case to start with?
Start with one repeated workflow that ends in an action: competitor research that creates a ticket, YouTube research that drafts an outline, a daily review that updates a project board, or a comment workflow that prepares replies for approval.
Why does Eric Siu criticize one-off reports?
Because reports create more homework unless they move into a workflow. A useful agent should research, decide what changed, draft the next action, and put that action where the operator or team will actually review it.
Should Hermes use Slack, Discord, Telegram, or Notion?
Use the surface where the work already happens. Discord can work well for solo agent HQs. Slack is usually better for teams that already live there. Notion or Linear can be better when the output is a task, decision, or project artifact.
What does it mean to skillify a workflow?
To skillify a workflow means saving a repeatable process as an agent skill: when to use it, what inputs it needs, which tools it can call, what output it should produce, and how success should be checked.
What are the main safety caveats for Hermes and OpenClaw style agents?
Start read-only, limit credentials, avoid autonomous public posting until trust is earned, keep logs, require approval for messages and payments, and separate research from execution when the action can affect customers or production systems.
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