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DirectorAlex

ModeHuman decides · agents code

Time zonesUS / UK / EU / Gulf

Open to a few conversations a year

00/Briefing

Alex Sokolov

Alex

Product leader, engineer by training

DirectorAlex designs, decides, directs
CrewAI agents write the code

AI is taking over doing things right. Doing the right things is still a leader’s job.

I find where AI agents pay for themselves, and help you build the first one.

I’m Alex, a product leader and engineer by training. AI agents are my engineering team: I design, decide and direct. I help CEOs, COOs, CIOs and CTOs pick the agent work worth paying for, name who owns each number, and plan the first pilot.

Email Alex See how I help
Alex Sokolov
Director Alex designs, decides, directs
Crew AI agents write the code

01/Telemetry Facts on record

CH-01Track record

18+years in IT

In management
14 yrs
Teams of
80+

CH-02Physics game

8 daysblank project to 9 levels

Test scenarios gating every step
103
Decision records kept
46
Creature designs run to calibrate difficulty
15,309

CH-03Persona engine

3memory layers in an open-source AI persona engine

Reasoning
inner monologue
License
MIT license
Tested by
AI judge

CH-04Simulation

15social-science models in one simulation

Automated benchmark test
each model

02/Mission profiles 4 profiles

How I help

Not sure which format fits? Email one line about your company. I’ll suggest where to start, or tell you that you don’t need AI there yet.

MP-01Entry profile Start here

AI Opportunity Teardown

For
CEOs, COOs, CIOs and CTOs of SMB and mid-market companies who keep hearing “we need AI agents” and want a straight answer.
Problem
Vendors pitch tools. Nobody has mapped where agents pay for themselves, or whether you need them at all.
Book a Teardown by email

Payload

  1. 01
    A 90-minute working session with you and one or two of your leads, on the numbers you choose to share (NDA on request)
  2. 02
    A written memo within 3 business days:
    • your top agent opportunities, ranked by P&L impact and effort
    • a “Do we need AI here at all?” verdict for each one
    • an outline of the first pilot
Timeline1 week
FeeFixed

Quoted after a 30-minute call. 100% credited toward a Strategy Sprint if you book one within 30 days.

MP-022–3 weeks

AI Agent Strategy Sprint

For Leadership teams ready to commit budget and owners to AI.

AI programs get announced without a number, an owner or a date. Pilots stay demos and never reach daily use.

  • Agent opportunity map
  • KPI tree linked to the P&L, owners set by RACI
  • Model-agnostic pilot architecture
  • Vendor and build-vs-buy review
  • Rollout plan

Usually scoped after a Teardown

Discuss a Sprint
MP-032 weeks

AI Engineering Team Setup

For Founders and product teams who pay for AI tools but don’t yet ship product with them.

You pay for AI subscriptions but have no operating system around them. The agents produce output, not product.

  • Design-interview template, 200+ questions
  • Decision-record log
  • Test-gate checklist
  • Agent roles and handoffs
  • Cost guardrails

Set up on your first real project

Ask about setup
MP-041 day a week

Fractional CPO for AI Products

For Seed to Series A AI startups, and corporate AI units without a product leader.

You have strong engineers but nobody owns the product’s economics, so demos drive the roadmap.

  • Strategy and roadmap tied to a KPI tree
  • Pricing and discovery hypotheses tested on data
  • Team design and hiring criteria
  • Decision log
  • Weekly operating cadence

3-month minimum · live overlap with US Eastern mornings

Talk about a fractional role

I take on one or two client engagements at a time.Open to a few advisory, co-founder and C-level conversations a year.

All formats in detail

03/Method Operating procedure

Data first. Expert second. IT third.

Schematic 03-AFrom question to build

Gate 00Before anything gets built, five questions

  1. Q1What problem are we solving?
  2. Q2Does it exist?
  3. Q3Is it the one we thought?
  4. Q4Does it need solving?
  5. Q5What is the cheapest fix that works?
Stage 01FirstData
Stage 02SecondExpert
Stage 03ThirdIT
Off-ramp · before ITSometimes the answer is a process change and a spreadsheet.
P-01

Prove the problem before you price the solution.

Five questions come before anything gets built. Sometimes the answer is a process change and a spreadsheet.

P-02

Every initiative gets a number, an owner and a date.

A KPI tree linked to the P&L, with owners set through a RACI matrix. Without it, an AI program is a press release.

P-03

Decisions are the new bottleneck.

With agents, going from zero to a working MVP takes weeks. The value arrives only when leaders change roles and processes around the new tool.

P-04

Agents amplify management, good or bad.

If a manager can’t set a task and accept a result from people, agents won’t fix that. A team that plays together beats a team of stars.

RENTED PIERS? OLD CROSSINGNEW BRIDGEFORD UNTIL SOMEONE CLOSES ITBUILT OVERNIGHTNO BRIDGE NEEDED

The bridge rule

With AI you can now build a bridge overnight. But if nobody closes the old crossing, traffic stays where it was. If the river can be forded, you don’t need a bridge at all.

And don’t build on rented piers: today’s model prices are subsidized by investors and can change, so design for switching providers without a rebuild.

How I work with AI agents

04/Mission log 3 entries

Selected work

LOG-01B2B caseDemo on request

LLM Analog Finder

Find the substitute before you buy the duplicate

In large inventories the same kind of part often sits under different names, units and spec formats. Buyers can’t see the equivalent already on the shelf, so they order new stock.

An AI pipeline that cleans messy inventory master data, merges duplicates and finds functional substitutes for the item you need. Parameters first; a category-expert LLM steps in only where the algorithm is unsure.

Built
My idea and architecture. I wrote the first prototype, for cables, from scratch in Python and JavaScript
Scaled
In production at an industrial company. A team of engineers and AI agents scaled it under my lead
Tested
Open retail catalogs; synthetic inventory data in SAP and other ERP formats
76kcatalog items run through the pipeline at an industrial company
1 in 3items got a substitute or a conditional match
72%of “analogs” in electrical were duplicates of the same part
2 daysto onboard a new product group; the first build took 3 weeks

Why it matters One architect with AI agents builds a working prototype; a team scales it on a proven design. A new product group becomes configuration, not development.

Production numbers from an industrial company. Client name under NDA.

Talk about your catalog
LOG-02Open sourceMIT license

Aristarkh Gradov

An open-source AI persona that thinks before it speaks

Aristarkh plays a sharp-tongued media producer who reviews creators’ projects. He plans every reply in a private inner monologue before he answers. Trust, fatigue and mood shape his tone. He tells you he’s an AI.

Why a CEO should care

Memory
client history and policies
Two-pass reasoning
explainable decisions
AI-judge testing
quality control
A cheap router model that decides when a costly step is worth it
the basis for unit economics per request

MIT-licensed · a reply guard enforces honest AI identity · end-to-end tests of the live bot, graded by a separate model

Explore the architecture
LOG-03Physics gamePrinceps Games

Evolution Workbench

A physics game I designed and directed. AI agents wrote the code.

The agents also helped with the in-game text. Build an alien creature from parts and send it across hostile worlds. When it falls, an “autopsy protocol” tells you why, in numbers.

9 levels / 8 daysblank project, a from-scratch rewrite after a first prototype
15,309creature designs run through the physics to calibrate difficulty
103automated test scenarios gate every step
46decision records · no third-party assets

Why it matters The decision log and test gates behind this game are the system the AI Engineering Team Setup puts in place.

Browser demo coming to itch.io · desktop browser only

Watch the early devlog

All projects: business cases, agent lab, games

05/Transmissions Manifesto

  1. TX-01

    Managers do things right; leaders do the right things.The line goes back to Warren Bennis. AI is about to take over the first half.

  2. TX-02

    No P&L number with a name and a deadline attached means no transformation. Just a press release.

  3. TX-03

    Code is no longer the bottleneck. Decisions are.

  4. TX-04

    If you can’t manage people, you won’t manage agents.

  5. TX-05

    Sometimes the right answer to “which AI do we need?” is “none, use Excel.”

06/Briefings 6 talks

Speaking

Keynote · panel · fireside chat · workshop · in English (C2) · remote worldwide · in person by arrangement

Speaker kit: bio in 3 lengths · photo

Invite Alex to speak Talks and bios
  1. T-01Robot Managers Do Things Right. What AI Agents Won’t Replace.
  2. T-02My Engineering Team Is AI Agents: A CPO’s Operating Model.
  3. T-03No P&L Number, No Transformation.
  4. T-04Building an AI Persona That Thinks Before It Speaks.
  5. T-05The One-Person Company: Running a Business with AI Staff.
  6. T-06Sometimes the Right AI Answer Is “Not Here.”