Полная гайд как я автоматизировал бизнес с Hermes Agent

Business strategy & operating model (AI-first “employee swarm”)

• Claims an “AI First Company” trajectory:
  • move from people doing routine work
  • to a model where a small team makes decisions, while AI agents execute most operations
• Positions Hermes Agent as a foundation for multi-agent systems:
  • multiple specialized agents run autonomously
  • agents coordinate via one control layer (“one person controls them”)
  • agent outputs are validated to avoid unreliable “assistant-like” behavior
• Core management principle: task formulation + measurable quality control matter more than:
  • model choice
  • having “the most subscriptions”

Execution framework / playbook (method behind the system)

Build “AI employees” using strong human practices + QA

1. Find a truly strong professional practice (e.g., top-performing competitors’ articles/strategies).
2. Extract key “methodological meanings” (what actually drives results).
3. Translate them into a work order (agent instructions).
4. Add quality control checks so the agent verifies output vs an expected result.
5. Use a multi-stage evaluation flow (optionally using more capable/expensive models for checking).

Create a “Second Brain” (company knowledge base)

• Store business context in Markdown (.md), including:
  • goals, products, clients, rules, KPIs, prior decisions
• Use an MD index + wikilinks to build an internal knowledge map for targeted retrieval.
• Keep the knowledge base updated as decisions change.

Turn repeatable tasks into “skills”

• Any task performed at least twice should become a reusable skill.
• Agents can be configured to update skills automatically when corrections occur.

Context engineering

• Provide the agent the right context from the second brain.
• Compress/refresh sessions to reduce token waste.
• Validation approach:
  • run the same prompt through multiple models/windows
  • compare results

Process checklist (implied steps)

1. Choose one task where you can judge agent performance vs a human.
2. Build the second brain (knowledge + KPI context).
3. Draft a work order from best-in-class practices.
4. Implement QA checks (possibly multi-model).
5. Deploy as scheduled automation (“Chrome Jobs”) so it runs like a real employee.
6. Convert successful flows into skills; continuously retrain and refine.

Product / marketing / ops automation examples (concrete use cases)

Content production “facility / content factory”

• Autonomously generates long-form video scripts (teleprompter-friendly; claims “no AI patterns”).
• Builds carousel posts and funnels into a free advertising offer (“Viral Media”).
• Also describes an SEO workflow:
  • find search topics
  • write & edit
  • analyze weekly
  • update methodology

SEO department replacement (example performance)

• Hermes replaced an SEO department; claims weekly analysis and self-revision.
• Metrics claimed:
  • 10,000 visitors/month from Yandex + Google
  • continued traffic growth; expected ~50% increase this month
  • at least one business at ~3 million revenue (and another “couple of million”)

Sales automation / lead qualification (clinic network)

• Clinics (“Miss Laser”) use AI for call volume:
  • 30,000+ calls/month
  • AI qualifies cold leads and books when the prospect says “yes”
  • a live operator completes signup
• Unit economics claimed:
  • recording cost reduced from ~1,000 RUB to <100 RUB
  • implied payback (no exact margin provided)

GRM + AI replacing sales department

• Team size claimed:
  • 3 people in Smyslokod doing work of ~30 people
• Metrics claimed:
  • >10 million RUB/month revenue
  • video-to-purchase conversion: 12%
  • average check: 70,000 RUB
• Sales approach:
  • “no sales department”
  • sales handled by GRM + AI with gentle guidance (not pushy selling)

Financial reporting & cost optimization

• Automates monthly financial analysis (cash flows, P&L, VAT).
• Claims:
  • cut company expenses by 30%
  • found expense insights a financier hadn’t seen “for years”
• Also describes monthly agent-run cash flow → PNL compilation (replacing manual manager analysis).

Weekly marketing strategy generation

• Agents propose promotions/scripts and operational rules for staff.
• Example: “2+1” promotion (“Troynichok”)
• Claimed outcome: 30% increase within a month

Personal performance assistant (“where is my money”)

• Daily assistant reviews:
  • what was done yesterday
  • plans for today/tomorrow
  • suggests focus to maximize progress toward financial goals

Metrics, KPIs, and targets mentioned

• Personal income:
  • tripled over the summer
• Content / SEO:
  • 10,000 visitors/month
  • ~50% expected traffic increase this month
• Revenue:
  • ~3 million RUB revenue (one business) + “another couple of million”
  • Smyslokod: >10 million RUB/month
• Sales funnel:
  • 12% video → purchase conversion
  • 70,000 RUB average check
• Clinics:
  • 30,000+ calls/month
  • recording cost reduced: ~1,000 RUB → <100 RUB
• Financial operations:
  • 30% expense reduction without changing operations
• Agent scaling:
  • current: 400+ autonomous agents
  • target by end of year: 1,000 agents
• Token/cost optimization:
  • claims up to 80% token savings via routing to cheaper/free models for routine tasks

Operations / controls: preventing “token waste” and wrong outputs

Explicit warning: context overload can cause hallucinations and wrong references, “paying” in tokens.

Mitigations:

• start new “sessions” to compress context
• implement QA checks (including cross-agent / separate judge agents)
• use:
  • weaker models for generation
  • stronger ones for verification/humanization when needed
• use a dedicated server for stability (24x7) and easier remote operation

Technology / process architecture (high level)

Hermes Agent (“Germes” mentioned)

• Runs on a server (24x7) with:
  • a desktop app
  • or a Telegram interface
• Connects multiple model providers and routes tasks to different models.

Second brain storage

• MD documents + MD index + wikilinks
• Optionally synchronized/shared with a team via MCP.

Automation

• “Chrome Jobs” = scheduled skills that execute tasks (e.g., write & publish, run analysis).

Model routing

• Strategy/planning: GPT / Astra (described)
• Plans/implementation: Claude / Fable (mentioned)
• Code: “clot opus”
• Simple tasks/parsers: “sonet”
• Some restricted tasks via a gateway approach
• Emphasis:
  • cheaper models for routine tasks
  • smarter models where quality is critical

Pricing / infrastructure notes (business execution lens)

• Mentions server options (KVM1/2/4/8) with increasing CPU/RAM/disk for agent load.
• Recommends starting smaller (not the largest) and upgrading later as scaling continues.
• Mentions avoiding the “Russia” region for some model availability (practical deployment constraint).
• Notes token economics: prefers subscription-based access vs raw API for cost reasons.

Presenters / sources

• Artemy Miller (creator of Smyslocode), who mentions:
  • startups: “Aishka Ia”, “Piratic AI”
  • clinic network: “Mrs. Laser”