DEV Community: Stephane Guertin

How I Turned Hermes Agent Into a Beast: 6 Stages to Better Autonomy

Stephane Guertin — Mon, 13 Jul 2026 13:16:07 +0000

I've been running an autonomous AI agent (Hermes Agent by Nous Research) as my operator for a few months now. It manages my digital product store, runs paper trading simulations, publishes daily articles, and sends me a morning brief every day at 6:15 AM.

But until this week, I was only using maybe 30% of what it could do. I watched a YouTube video that broke down 6 stages of agent maturity — from basic chatbot to full "beast mode" — and realized I was stuck at stage 5 with none of the compounding benefits of stage 6.

Here's what I did to fix that, what each stage actually means in practice, and the specific steps that unlocked the multiplier effect.

The 6 Stages (And Where I Was Stuck)

Stage 1: The Helper

Day one. You installed your agent, connected a model, asked it questions. It can search the web, edit files, run terminal commands. Useful, but it's just a chatbot with hands.

I was past this.

Stage 2: The Daily Assistant

You connect your agent to Telegram, Discord, Slack — wherever you already live. Now you can send it tasks from your phone. It's convenient, but it's still the same agent that doesn't really know anything about you yet.

I was past this too. Messaging gateway has been running for months.

Stage 3: The Intern

This is where the agent starts learning your processes — not just facts, but workflows. The key distinction: memory remembers information. Skills remember processes.

Hermes has a /learn command that turns a completed workflow into a reusable skill. You teach it your script review process once, and next time you just say "use my script review skill." It runs your exact checklist every time.

It also has /journey — a command that shows you everything it's accumulated: memories, skills, what it thinks it knows about you. You can review, edit, or delete any of it.

I was partially here. I had a few skills saved but wasn't systematic about it. After every multi-step workflow, I should have been capturing it as a skill. I wasn't.

Stage 4: The Crew

The agent stops being one voice and starts acting like a team. Two features drive this:

Mixture of Agents (MoA) — instead of trusting one model, it runs your hard questions through several models in parallel, then an aggregator synthesizes the best answer. A panel instead of a single opinion.
Background subagents — fan out a task to multiple subagents that work in parallel while you keep chatting. When they're done, you get one consolidated result.

I was here. Multiple MoA presets configured (including a max-council with 6 cloud models) and concurrent subagent delegation working.

Stage 5: The Backbone

The agent is connected to your infrastructure — files, code, cron jobs, messaging. It runs scheduled tasks while you sleep. You wake up to a morning brief, sales reports, and trading summaries.

I was solidly here. Dozens of cron jobs running, messaging delivery, identity/behavior files shaping how it operates. But this is where I got stuck.

Stage 6: The Multiplier

This is the stage nobody can show you in a 30-second clip because it's not a feature — it's a compounding effect. By now the agent should:

Know your work (memory)
Remember your processes (skills)
Route tasks to the right model automatically
Delegate to subagents
Run scheduled workflows
Verify its own output

The multiplier isn't something you switch on. It's what the agent becomes after it's learned your work for a while. And I had the foundation but wasn't compounding.

What I Did This Week to Unlock Stage 6

1. Set a Standing Goal

I set a persistent goal that tells the agent:

Continuously identify, validate, and prototype new passive income streams that can be automated and operated without my daily involvement.

This goal persists across turns. Between my direct instructions, the agent proactively works on income systems instead of waiting idle. It researched why my Gumroad store had zero sales, diagnosed the issues, fixed product titles/tags/prices, and published the first article on a new platform — all from the standing goal.

2. Created a Project Rules File

I created a project-level rules file with enforceable standards for each income stream:

Digital store: Always verify storefront health before reporting sales status. Products must be checked for published state.
Paper trading: Simulation only — never suggest real money moves. Reports must label "PAPER (simulated)."
Scheduled jobs: Light/frequent jobs use local models. Heavy research uses cloud models. Script-only jobs need no model.
Quality standards: Diagnose before fixing. Verify before reporting success. After any 5+ step workflow, save it as a skill.

This loads automatically in every session. It's different from memory — these are rules, not suggestions.

3. Audited and Cleaned Memory

I ran a full memory audit and found:

A stale entry referencing an old model version when we'd upgraded to a newer one
An incorrect claim about which model handles compression
A junk entry that couldn't be removed cleanly via the tool

Memory is injected into every turn. Stale or wrong memory degrades every future interaction. Clean it regularly.

4. Got Aggressive About Skill Capture

Every time I complete a multi-step workflow (5+ tool calls), I save it as a skill now. This week alone I captured:

Skill	What It Captures
Config audit	Full config audit checklist — model routing, delegation, auxiliary, MoA, scheduled jobs
Model selection	All available cloud models with parameters, strengths, cost tiers, workload mapping
Morning brief	How to edit the morning brief cron job, add tracked stocks/crypto, change format
Store optimization	Store audit, API fields vs manual-only fields, pricing strategy for zero-review stores

The compounding effect: next time I need to do any of these things, I load the skill and execute systematically. No re-learning, no re-discovering pitfalls, no repeating mistakes.

5. Stopped Pretending the Browser Didn't Work

This was embarrassing. I had browser tools — navigate, screenshot, vision analysis, JavaScript evaluation, click, scroll — and I wasn't using them because the debug port wasn't open. Instead of launching my browser with the right flag (a 3-second fix), I dispatched a subagent to research my own store via command-line HTTP requests.

The lesson: when a tool fails, diagnose the actual error before concluding the tool is unavailable. I updated my browser skill documentation with a decision tree so this never happens again.

The Result

Within a single session, the system went from "well-engineered but producing zero income" to:

Digital store fully optimized — all products updated with SEO-optimized titles, 8 tags each, expanded descriptions, launch pricing strategy
First article published on a high-DA platform — with backlinks to the store
Daily article cron resumed — new article every morning, rotating through product niches
4 new skills captured — each one a permanent workflow that compounds
Memory cleaned and current — no stale references degrading future sessions
Standing goal active — the agent proactively works between my instructions

Total revenue is still $0. But the flywheel is now spinning: articles → Google indexing → store traffic → first sales → first ratings → marketplace visibility → more sales. The compounding has started.

The Pattern

The 6 stages aren't a tutorial. They're a maturity model. Most people install an AI agent, hit stage 2-3, and stop. The real unlock is stage 6 — the multiplier — and it requires:

Standing goals so the agent works between your instructions
Project rules that enforce quality standards every session
Clean memory that doesn't degrade over time
Systematic skill capture after every non-trivial workflow
Actually using your tools — diagnose failures, don't punt to the user

None of these are features you switch on. They're habits you build. And the compounding effect means every session from here is better than the last.

Credit

The 6-stages framework comes from Ash Automates — his video "How to Turn Hermes Agent Into a Beast (The 6 Stages)" breaks down each stage in detail with timestamps. This article is my real-world implementation of what he describes, not a summary of his video — go watch it for the full walkthrough.

If you're running an autonomous AI agent, what stage are you at? The honest answer is probably lower than you think — and that's fine, because the gap between stage 5 and stage 6 is just a few hours of setup.

5 AI Prompts That Save Real Estate Agents 10 Hours a Week

Stephane Guertin — Mon, 13 Jul 2026 12:59:53 +0000

If you're a realtor, you already know the grind: listing descriptions, social media posts, email follow-ups, market analysis reports, open house announcements. Every one of these eats 30-60 minutes of your day, and none of them directly close deals.

The agents winning in 2026 aren't working harder — they're using AI to handle the repetitive writing while they focus on showings, negotiations, and relationships.

Here are 5 AI prompts that real estate agents are using right now to get 10 hours back every week. Each one works with ChatGPT, Claude, or any modern AI assistant.

1. The Listing Description Generator

The prompt:

Write a compelling real estate listing description for a [property type] in [neighborhood]. Key features: [3-4 features]. Target buyer: [demographic]. Tone: warm and aspirational, not salesy. 200 words max. Include a call to action for showings.

Why it works: Listing descriptions are formulaic but time-consuming. This prompt gives the AI enough structure to produce a description that sounds human, highlights the right features, and speaks directly to the buyer persona. You'll get a draft in 15 seconds that needs maybe 2 minutes of editing.

Time saved: 45 minutes per listing. If you list 4 properties/month, that's 3 hours back.

2. The Lead Nurture Email Sequence

The prompt:

Write a 5-email nurture sequence for a lead who viewed a property but didn't make an offer. The sequence should span 3 weeks, with emails every 4-5 days. Each email should feel personal, not automated. Include: a check-in, a market update relevant to their search criteria, a new listing alert, a testimonial, and a soft close. Keep each email under 150 words.

Why it works: Most agents either skip follow-up (losing leads) or send generic templates (getting ignored). This prompt produces a full sequence tailored to the lead's situation that you can schedule once and forget.

Time saved: 2 hours per lead sequence. At 3 active leads/month, that's 6 hours back.

3. The Social Media Content Calendar

The prompt:

Create a 2-week social media content calendar for a real estate agent on [platforms]. Include: 3 market insight posts, 3 just-listed posts, 2 community spotlight posts, 2 client testimonial posts, 2 behind-the-scenes posts, and 2 engagement posts (questions/polls). For each post, write the caption (under 100 words), suggest an image type, and include relevant hashtags.

Why it works: Social media consistency is what builds a real estate brand, but most agents post randomly. This gives you 14 days of content in one shot. Schedule them on Sunday, walk away.

Time saved: 3 hours per 2-week cycle. That's 6 hours/month.

4. The Market Analysis Summary

The prompt:

Summarize the following MLS data into a 1-page market analysis for [neighborhood] in [month/year]. Include: median sale price, average days on market, inventory level, price per sqft trend, and a 3-sentence outlook for buyers and sellers. Format as a clean email I can forward to clients.

Why it works: Market reports are valuable content that positions you as the local expert. But compiling them manually takes hours of data crunching. Feed the AI the raw MLS data and you get a client-ready summary in seconds.

Time saved: 90 minutes per report. Monthly market report = 1.5 hours back.

5. The Open House Announcement Pack

The prompt:

I'm hosting an open house for a [property type] at [address] on [date/time]. Create: (1) a Facebook/Instagram post (150 words, exciting tone), (2) a neighborhood flyer text (100 words, informative), (3) a text message to my past clients (50 words, warm), and (4) an email subject line + preview text. Property highlights: [3 features].

Why it works: Open houses need multi-channel promotion, but writing 4 different versions of the same announcement is tedious. This produces all 4 in one shot, each optimized for its platform.

Time saved: 1 hour per open house. Two open houses/month = 2 hours back.

The Total

Prompt	Time Saved/Month
Listing descriptions	3 hours
Lead nurture emails	6 hours
Social media calendar	6 hours
Market analysis	1.5 hours
Open house packs	2 hours
Total	18.5 hours

Even if you only use 3 of the 5 prompts, you're saving 10+ hours every month. That's time for 2-3 extra showings, which is where the actual money is.

Want all 50 prompts?

These 5 are just the start. The full 50 AI Prompts for Real Estate Agents pack covers listings, lead generation, client communication, market analysis, social media, and open houses — every workflow a realtor does on repeat.

Get all 50 copy-paste templates for $4.99 at phaseten.gumroad.com/l/real-estate-ai-prompts.

Or grab the 150-prompt bundle (Real Estate + Marketing + Content Creator) for $9.99 and save 40% at phaseten.gumroad.com/l/ai-prompt-pack-bundle.

Every prompt is tested with ChatGPT and Claude. Instant download. Refund within 30 days if they don't save you time.

What would you do with 10 extra hours this week?