From Vladimir Nagin — founder of LeadUp AI, over three years working with AI agents, trained 500+ entrepreneurs in business automation.

Here's a simple exercise. Take your hourly rate. Multiply by the dozens of hours per month that currently go to typical tasks — sorting through email, meeting preparation, execution monitoring, digests.

If your hour is worth $100 — calculate for yourself how much you're actually paying for routine every month.

That's exactly the time a properly configured AI assistant gives back to the executive.

This isn't an advertising promise. This is arithmetic that doesn't depend on the size of your company.

Where your time actually goes

If you try to honestly break your week down by hours, the picture is the same for most executives: a significant share goes to meetings, an even larger share — to communications (email, messengers, team chats), and a noticeable chunk — to administrative load that brings the company not a single new client and doesn't change a single strategic decision.

Research confirms this in numbers. According to Harvard Business School (Porter & Nohria, How CEOs Manage Time, HBR 2018), CEOs spend about 72% of working time on meetings — almost three quarters of all working hours. According to McKinsey Global Institute ("The Social Economy," 2012), the most "loaded" executives spend 8–9 hours a week on email alone.

For what you created the business for — strategy, key clients, non-standard decisions — noticeably less time remains than you'd like.

You're working all this time. But most of the time you're working where your value isn't maximum.

The most non-obvious thing

There's one fact that changes the perspective on this whole picture.

A significant part of an executive's decisions are routine. Not unique, not requiring deep expertise. The same decisions that repeat again and again. Fundamentally any of them could be made by anyone — provided that "anyone" has the context of your company.

Context — that's where the problem used to be. A chatbot without context is useless. A new employee accumulates context over months. But a new-class AI agent — like Hermes Agent from Nous Research — is capable of continuously accumulating the context of your business and never losing it.

What is Hermes Agent? Open-source AI assistant from Nous Research with three differences from regular chatbots: direct tool invocation (email, CRM, messengers), self-learning through a self-reflection loop after each of your responses, persistent memory that doesn't reset between sessions.

Three levels of what AI can be

Most people are stuck at the first level — reactive. Wrote — got an answer. Stopped writing — the tool went silent. That's how ChatGPT works in regular chat mode. This is useful. But it's not what we're talking about.

The second level is proactive. The agent notices an anomaly on its own. Sees that one department's KPI has deviated — and without your request forms three hypotheses and a draft letter. You just need to read and decide: send or not.

The third level is autonomous. The agent runs projects itself and informs you after the fact. Five new clients in a week, meetings scheduled, contracts sent — you get a summary at the end of the week. Critical decisions — budgets, personnel matters, major contracts — remain yours. Everything else the agent closes independently.

Right now, most companies — even those who actively "implement AI" — are at the first level. The gap between the first and third levels is exactly the competitive advantage that Sam Altman and other market leaders talk about.

Five tasks the assistant closes right now

This isn't theory. Everything listed works today, without writing code.

1. Information analysis. Morning summary of incoming emails with prioritization — not 90 minutes on email, but 5 minutes on a summary. Competitor monitoring, tracking KPI anomalies, industry news digest.

2. Communications. The agent writes draft replies to letters, prepares deal follow-ups, reminds about deadlines. You approve, not write from scratch.

3. Decision-making. Scenario modeling: "what changes if we raise prices 15%" — the agent prepares options with pros and cons, you make the decision based on an already-prepared analysis.

4. Delegation. Distributing tasks among employees with deadlines, execution monitoring. The agent won't forget to ask for a status — unlike you, occupied with another meeting.

5. Learning from your style. This is what sets Hermes Agent apart from everything before. After each interaction the agent analyzes your decisions and saves them as a skill. After 2–3 weeks it matches your style in 80% of typical situations.

A real case: 90 minutes to 5 minutes

One of the cases I broke down at a recent intensive — a head of a marketing agency, team of 25+.

Before Hermes: 60+ emails a day, 90 minutes every morning just sorting through email. Plus preparing for each meeting — often postponed and done hastily. In 30% of meetings they arrived without full context.

After two weeks with Hermes: in the morning you open a summary — 5 minutes, everything is clear, what's urgent and what can wait. 30 minutes before each meeting a briefing arrives in Telegram: history of the relationship with the counterparty, negotiation status, potential risks. No surprises.

The CEO commented: "Didn't realize I had an assistant — until Wednesday morning when I opened email and saw a ready summary already with priorities."

How to calculate your ROI

The formula is simple.

Take your real hourly rate — the one you value your time at as an owner or executive. Estimate how many hours a month go to routine that could theoretically be delegated with the right context.

Compare with what the infrastructure costs: Hermes Agent is open-source, free. Server or Mac Mini — one-time or small monthly cost. Models for the agent via Ollama — about $20 per month.

One freed hour of your time is worth more than a month of operation of the entire infrastructure.

Why now

In May 2024 the heads of the largest technology companies almost simultaneously started talking about AI agents appearing for every person. Sam Altman formulated it most precisely: "AI agents aren't tools, they're your new management team."

Less than two years have passed since then. According to Deloitte "State of AI in the Enterprise" (2025–2026), 85% of surveyed companies expect to customize autonomous AI agents for their business in the coming years. About 75% plan to deploy agentic AI in their processes within the next two years.

But most stopped at the first step. They tried. Didn't get what they expected. Decided "it's too early."

The gap between those who've gone through the second and third levels and those stuck at the first will only grow. Because an agent that's been working with you for six months is incomparably more effective than one you just launched.

Memory accumulates. Skills improve. The context of your business deepens with each week.

Where to start

The first step isn't "implement a system." The first step — take one task.

Not email and Telegram simultaneously, not competitor monitoring plus task distribution. One specific function that takes up a significant amount of your time and recurs regularly.

Explain to the agent how you want this to work. Give it a week. Watch it learn. Correct it.

Then take the second task.

That's exactly how the transition from reactive to proactive happens. Not in one day, but in weeks — already noticeable. In a month — tangible.

Further in the series

This article is the first in a series on new-class AI agents. In the other four:

Vladimir Nagin — founder of LeadUp AI, author of the Neuromasterskaya 2.0 program. Over three years working with AI agents — from the first prototypes on Flowwise in 2024 to multi-agent teams on Paperclip today. More than 500 entrepreneurs have completed his business automation courses.