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

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:

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.