HERMES AGENT CONSULTING

Hermes Agent for a team that needs more than a chatbot.

Your team shouldn't have to explain the business from scratch every time they ask AI for help. Imagine asking a question in the place you already work, getting an answer grounded in the right records, and seeing the next step prepared for someone to check.

That's what we're working towards with Hermes Agent inside CLCK. We can help your team decide where an assistant would make a difference, set it up around your tools and working habits, and keep people in charge of what it can do.

The installation is one part. Hosting, model choice, access, reusable instructions, training, approvals and ongoing care determine whether people will actually trust and use it.

Talk through a Hermes setup Read our Hermes field notes

Hermes Agent consulting for Australian businesses and consultants. We aren't Nous Research or official Hermes support.

WHO IT’S FOR

When your team has outgrown the blank AI chat window

If people spend their week hunting for context, briefing one another and moving information between tools, there's a reason to explore an assistant. The question isn't whether AI can answer one clever prompt. It's whether it can help your team finish a real job, then leave a result you can check.

Your team already asks AI for help

People can draft in a chat window, but each person still has to find the background and move the result into the right system. You want help that lives closer to the conversation and the work that follows it.

Work is scattered across people and tools

Sales notes sit in one place, client decisions in another and the current procedure in someone's head. Hermes is worth considering when bringing those pieces together would save your team repeated effort.

You need a way to keep it running well

Installing software is the easy bit. Your team also needs clear access, review points, training and someone to handle problems or update the working instructions when the business changes.

WHY HERMES IS DIFFERENT

What changes when AI can work from your team's context?

ChatGPT or Claude in a browser can help with a standalone task. Hermes adds a configurable way to bring an assistant into the team's work, with connected tools, retained instructions and defined limits. The value depends on how you set those pieces up.

Bring it into the team conversation

In a supported chat app, people can ask for help in the thread where the job is being discussed. We use it in Slack and Zulip. The available connection and how it behaves depend on the platform and the setup.

Give it the right sources, not every source

Supported APIs, MCP servers and other connections can let Hermes check records or prepare work in your systems. The permission for each job matters more than the length of a connection list.

Keep the agreed way of working

Skills can give a repeated job its sources, steps, checks and approval point. Selected memory and project notes can help with continuity. People decide what deserves to be kept, reviewed or corrected.

CLCK EARLY-ADOPTER EXPERIENCE

What we've learnt putting Arlo to work inside CLCK

Our team uses Arlo in shared conversations to investigate questions, prepare client responses, research options and help with work in HubSpot. In marketing, it can take an idea through source checks, a draft and a website preview for a person to review.

What surprised us wasn't how quickly it could draft. It was how much time our team spent finding background, interrupting a more experienced colleague or repeating an explanation. Arlo helps with that work in the same place the question was asked.

That doesn't mean we let it run the business. We still check what it found, correct the instructions when they need work and approve client-facing or live changes. We're independent adopters, not Nous Research or official product support.

The installation is only one piece.

Where Hermes should run
Which team surface should own each type of work
Which tools and data it can inspect
Which actions need explicit approval
How skills, memory, logs and backups stay clean

What our own team asks Arlo to do

The value shows up between the question and the next step

These examples come from how we use Hermes inside CLCK. They show the work it helped with, what it checked and where a person still needed to decide. Our experience is hands-on, but we won't pretend every task works perfectly on the first try.

A technical answer without an interruption

A teammate asked Arlo to check a website's HubSpot domain connection and tracking. It found that the domain was connected, while tracking wasn't detected in the homepage test. Those are two different findings, and the second one isn't proof that tracking is absent from every page.

That distinction gave the team a starting point for the next check, without waiting for someone more technical to reconstruct the question. Arlo didn't change the client's account or say the problem was fixed.

Marketing work that returns ready for review

In our marketing work, an idea might begin in a team thread or content queue. Arlo can inspect what's already published, gather sources, prepare a draft and check the page in a preview. A person chooses the direction, edits the copy and approves anything that goes live.

It isn't one magic prompt. It is a way to keep the goal visible while smaller pieces of work move forward with a result we can check. See how we use Arlo for marketing.

Corrections that can carry forward

When we agree that a repeat job needs a better checklist, the change can be written as a skill for review. Next time, the assistant has the agreed sources, format and checks available instead of asking for the same briefing again.

We still review those instructions. A correction from one job shouldn't automatically become a rule for every client, and a saved skill doesn't make the result infallible.

IMPLEMENTATION DECISIONS

A setup your team can actually work with

Where it runs and who looks after it

A local machine may be enough to explore. For a team relying on recurring work, we assess an appropriate server or VPS, monitoring, backups and recovery. Hosting is a responsibility, not just a box to tick during installation.

Where people will actually use it

We choose a supported workspace that fits how your team talks and reviews work. A chat thread can keep the question, source and answer visible; a separate project record may still need to hold the durable decision.

Which AI provider fits the work

The model does the reasoning and writing; Hermes connects it to the work. We talk through provider options, privacy, usage costs and how much accuracy or speed each job needs. A single expensive route isn't automatically best for everything.

Who can see what and approve which action

Client delivery, sales and internal marketing don't all need the same context. We map access and approval to the job and keep private information within the right team or client boundary.

GUARDRAILS AND APPROVALS

Faster preparation, with people still in control

A good answer is one thing. Permission to send it to a client, change a CRM or publish it is another. We work out those boundaries with you before the assistant starts handling consequential jobs.

Start with work a person can check

A meeting brief, follow-up draft or weekly update has a clear owner and a visible result. It's a better starting point than giving an agent wide authority across every system.

Keep the decision with the right person

Sending a client email, publishing a page, changing a live client system or making a commercial decision is different from preparing a draft. The person with authority reviews the exact change before it happens.

Keep client and team context in its place

A shared chat app doesn't make every record shared. We separate client information, private conversations and tool access, and check what the underlying systems allow before connecting them.

Let the risk shape the workflow

Some tasks are safe to run inside a defined boundary. Others need a preview, a second check or an explicit decision. The goal is to avoid slowing every small job while keeping real consequences under human control.

SKILLS AND SOP MANAGEMENT

Teach it the parts of a job you shouldn't have to explain twice

A skill can name the sources to check, the steps to take, the result to prepare and the point where a person reviews it. That can make the next meeting brief or client update feel familiar, while leaving room for the team to correct the instructions.

Skills are reusable business routines

A skill might tell Hermes how to prepare a sales follow-up, QA a landing page, summarise a meeting, check a HubSpot list or hand a build brief to a developer.

Humans should curate the important ones

Hermes can help draft and improve skills, but mature implementations need human review. Otherwise temporary fixes, old assumptions and messy instructions become permanent noise.

A skill gives a job a repeatable path

For example, a call summary can identify actions, prepare a task brief and draft a client reply. Each output still needs a named place to go and a check before the next consequential step.

Different work needs different instructions

A client delivery question may need different sources and approval from an internal marketing draft. We attach the instructions and tool access to the job, rather than giving everyone one enormous shared rulebook.

OPERATING RULES

Make the everyday rules obvious to everyone

Should Arlo-like help appear only when someone asks? Who sees a recurring report? Which provider handles a sensitive task? When must the assistant stop and get a person to decide? These details shape whether the team uses it with confidence.

In our own work, the main conversation holds the goal and the decision. Detailed work happens in bounded tasks that report what they checked. It's one way to stop a long AI thread from losing the plot, without asking people to learn a new way of talking to each other.

Should Hermes answer every message, or only respond when mentioned?
Which tasks should use a stronger model, a faster model or a cheaper backend path?
When does a long conversation need a recap so the goal stays in view?
Which jobs can run autonomously, and which ones need a person to approve the next step?
How should recurring jobs report back so people can check them quickly?

MEMORY AND PROJECT CONTINUITY

Stop re-explaining decisions that the team has already made

Selected memory, project records and agreed instructions can help the assistant return to a job with the right context. They need to be curated. An old correction shouldn't quietly become a rule for the wrong client or team.

Keep the right background close to the work

A long project will outlast a single chat. The assistant needs a way to find the agreed decision, current record and open question, rather than relying on a stale recollection.

A handover the next person can trust

Selected memory and project records can help the next person see what was done and what still needs a decision. We distinguish lasting instructions from a temporary note, and keep client context where it belongs.

Less briefing, fewer handovers to reconstruct

When agreed instructions and decisions can be found next time, a team member doesn't need to brief from zero. The value shows up in the whole task, including less time checking and reworking it.

UPDATES, BACKUPS AND SUPPORT

Who looks after it once the team starts relying on it?

Someone needs to handle updates, broken connections, changing instructions, backups and the occasional problem the assistant can't resolve from inside itself. We discuss that responsibility as part of the implementation, rather than calling installation the finish line.

Update without losing the work you put in

Software updates need testing when the team depends on custom skills, connections and working rules. We plan what to check and how to recover if something changes.

Protect the setup, not only the software

The instructions, configuration and records of past work can matter as much as the installation. A recovery plan should include the parts your team would actually need to resume work.

Have a way to get help when Arlo is offline

If the assistant is unavailable, it cannot be the only place to ask for help with itself. We agree a separate way to spot problems and get the system checked.

SANDBOX FIRST

Test a real job before asking everyone to rely on it

Pick a recurring job with a known result. Try the assistant with representative information and agreed permissions. Ask the people who do that job whether the output helps and what they had to fix. A protected test environment is one option when live data or actions raise the stakes.

Map the first workflows

We choose a small number of jobs worth testing, usually the ones with clear inputs, repeatable outputs and a human who already knows what good looks like.

Build a sandbox implementation

Where the work warrants it, we test the chat interface, provider, connections, skills, approvals and reporting in a separate environment before introducing live data or actions.

Train the team and tighten the rules

The test should show what saves people time, what confuses them, where the permissions need tightening and which instructions need to be rewritten.

Move the right parts into live operations

We bring what we have learnt from our own service-business work. Your setup gets its own instructions and connections, not a copy of our private client context or internal systems.

PRACTICAL BUSINESS EXAMPLES

Start with the jobs that keep landing on someone's desk

Think of these as ways to spot a first job, not a list of integrations we turn on indiscriminately. We check what each connected tool supports, what access is appropriate and how a person will review the result.

HubSpot and CRM work

Create lists, check missing fields, draft internal notes, prepare workflow briefs, review landing page context and surface follow-up gaps. CLCK is a HubSpot Platinum partner, so we care about the CRM being clean before the agent touches it.

Sales and outreach support

Check campaign status, surface replies, prepare follow-up tasks, summarise buying signals and keep any external send behind a person until the process has proved itself.

Marketing, analytics and SEO

Turn data, screenshots, briefs and source material into checks, summaries, content briefs, reporting notes or research packs that a specialist can review.

Xero and admin reporting

Prepare overdue-invoice summaries, weekly finance/admin notes or recurring reporting packs, with access and approval rules shaped around your team’s privacy needs.

Meeting-note workflows

Turn notes, transcripts, screenshots or call summaries into project plans, task lists, agendas, build briefs or client follow-up drafts.

Automation platforms and webhooks

Use Zapier, Make or another automation layer to move work between systems, or let Hermes act as a processing layer behind a webhook when that architecture makes sense.

Google Workspace or Microsoft 365

Prepare document drafts, email summaries, agenda notes or folder-based workflows, with the right review points before anything client-facing goes out.

Scheduled checks and reports

At an agreed interval, the assistant can prepare a report, check for exceptions or bring open questions back to the person who owns them. Each job needs a destination and someone to review what it found.

WAYS TO WORK WITH CLCK

From a first workflow to a setup your team can depend on

We can help you decide where Hermes fits, put the agreed connections and instructions in place, train the team and plan for ongoing care. The work depends on your existing tools, your risk boundaries and who will own the system after launch.

Work out where it fits

We discuss the repeated jobs that cost your team time, the tools involved and what a finished result should look like. If a simpler HubSpot workflow or process fix is the better starting point, we’ll say so.

Set up and test the agreed work

We can help with hosting, model/provider choices, supported connections, skills, memory boundaries, approvals and training. We test a defined first workflow and show your team how to check and correct the output before relying on it.

Keep it working as the business changes

After the initial setup, someone needs to own updates, backups, connection issues and the instructions the team relies on. We can discuss ongoing care and what your own team would prefer to manage; the exact arrangement is agreed with you.

Not sure whether an assistant or a simpler workflow fix should come first?

BOOK A STRATEGY SESSION

Explore what sits around the assistant

FIELD NOTES

Hermes Agent for business

Read CLCK’s plain-English story of what we learnt using Hermes Agent inside the business.

Read the Hermes field notes

AI AGENTS

How AI agents help founder-led service businesses

Start here if you want broader AI agent use cases before choosing a Hermes workflow.

See practical AI use cases

AUTOMATION

Revenue operations automation and AI support

Best if the bigger issue is follow-up, admin, HubSpot workflows or sales operations behind the agent work.

See automation support

HUBSPOT

HubSpot implementation

Best if Hermes support depends on cleaner CRM structure, better data, clearer ownership or stronger reporting first.

See HubSpot implementation

PLAYBOOK

AI Prospecting Playbook

A good companion if agent support needs to improve research, outreach review, buying signals and CRM follow-up.

Get the playbook

FAQs

What is Hermes Agent consulting?

Hermes Agent consulting helps a business move from technical interest to a practical operating model: where Hermes should live, which workflows it should support, what it can access, which skills it needs and where human approval sits.

Is CLCK official Hermes Agent support?

No. CLCK isn’t Nous Research and we’re not affiliated with Nous or official Hermes Agent support. We help Australian businesses apply Hermes Agent from our own early-adopter experience using it inside CLCK.

How is Hermes different from ChatGPT or Claude in a browser?

A browser chatbot is helpful for one-off prompts. Hermes is an open-source, configurable agent layer that can live in team work surfaces, use tools, follow skills, connect to systems and keep working context through sensible memory or logging patterns.

Can CLCK help install Hermes Agent?

Yes, where it fits the business case. We usually start with the workflow and operating rules first, then choose the hosting, model/provider setup, team surface, access rules and backup process that make sense.

What should we use Hermes Agent for first?

Start with one repeated job your team already understands. Good examples include meeting prep, HubSpot checks, follow-up packs, campaign QA, admin summaries, project handovers or recurring agenda reports.

Can Hermes connect with HubSpot, Xero, Google Workspace or automation tools?

Often, yes, if there is suitable API, MCP or automation access and the privacy rules are clear. We prefer narrow, reviewed workflows before giving an agent broad access to business systems.

Can Hermes run recurring jobs?

Hermes can support scheduled or recurring work when configured properly. The practical question is what it should check, where it should report, who owns the result and what should happen if the job finds a problem.

Do you build fully autonomous AI agents?

We’re careful with autonomy. Hermes can prepare, draft, check, summarise and run some backend jobs, but public sends, live client-system changes, publishing and destructive actions should sit behind human approval until the workflow is proven and the risk is acceptable.

Who is this best suited to?

It suits SMEs, consultants and service businesses with roughly 5 to 50 team members, or enough workflow complexity to justify a serious implementation. If you only need a quick chatbot, this is probably too much.

What happens after Hermes is live?

A real implementation needs maintenance: skill hygiene, updates, backups, monitoring, recovery plans and regular review of which workflows are helping. Some of that maintenance can be assisted by Hermes once the system is configured safely.

Want to see whether Hermes Agent could take a repeated job off your team's plate?

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