Agentic GTM and Product Engineering: From 30 to 3

It used to take 30 people to run a GTM or product department. Today three is enough. That is not a forecast in agentic GTM and product engineering. That is my daily work.

In my earlier companies I built marketing, sales and product with big teams: hire, onboard, coordinate, hope. Today I do it differently. A few professionals plus a team of AI agents that work around the clock. I call this Get Multiplayer: how humans and AI agents run a department together. Three instead of thirty.

What you'll take away:

• Why AI made execution cheap, and what the real bottleneck is now.
• How Get Multiplayer works on both sides: GTM and product.
• Three concrete ways to put it into your company.

The thesis in one sentence

Anyone still building a GTM or product department with 30 people is building expensive, slow and without a system. The lever is no longer in execution. It is in leadership and context when ten agents run at the same time.

Why is execution suddenly the wrong bottleneck?

Because AI drove the cost per execution towards zero. A sales sequence, an account research run, a first pull request: hours of work are now minutes. The question is no longer who does the work but who leads it and on what context it runs.

Two years ago execution was the expensive part. Write a sequence, analyse a market, build a landing page, review a pull request: all hours, all heads, all salary. AI flipped that. An agent writes the sequence in minutes, researches a hundred accounts overnight, builds the first pull request while you sleep.

That moves the bottleneck. When ten agents run at once, the question is no longer "who does the work" but "who leads them and on what context do they work". Ten machines without leadership produce garbage ten times faster. That is garbage in, garbage out, just faster and at scale. Generic AI does not know your code, your Jira or your business. It produces plausible smoke. Every agent needs a shared foundation: the business and code context of your company, prepared for agents. I call this the Context Engine. It is the magic ingredient, not the model.

How do you build Get Multiplayer across GTM and product?

The same way on both sides. Three professionals take the lead, three to five agents take the execution, one Context Engine holds the knowledge. On GTM the result is Autonomous GTM, on product it is Autonomous Product Management. One idea, two applications.

The pattern is the same on both sides of the company. Good product, no system that scales with it. I call this state stuck in the middle: after product-market fit, before scale. The product works, but the foundation of processes and data is missing.

On the GTM side, this is how I build it:

1. Measure first, don't guess. Where is the real bottleneck? Positioning, pipeline, channels.
2. One central system instead of three truths. A clean CRM, one pipeline, one positioning in a single sentence.
3. Agents by bottleneck priority, not by hype. Three to five productive agents that run even when you are gone for a week.

On the product side, the result is Autonomous Product Management. Product decisions on real context, with AI agents, instead of gut. Knowledge today sits scattered across Jira, PRDs, stories and senior devs' heads. An agent that knows this context turns from code monkey to sparring partner. It opens the first PR-agent, builds the test-gap map and shows where shipping is safe and where it is not.

Both sides share the same foundation. On GTM the Context Engine lives as software that works like Jira for GTM teams. On code and product it sits inside an agent that studies your code.

What are the routes to the same outcome?

Learn it, have it built. And the software to go with it. You don't have to take every route, you have to know which one fits your phase. Learn and have-it-built are an escalation, from doing it yourself to having it done. Teklens runs alongside as software, whichever route you take.

• Learn: gtm.science is an open-source framework for GTM teams. In live cohorts you install Autonomous GTM hands-on in your company.
• Have it built: Pedalix builds your GTM and product department inside your company, done for you. A small team plus AI agents. The deliverable is a running system that stays. See my way of working.
• And the software to go with it: teklens.ai is software that joins your product team as AI agents. It knows your code, your Jira and your business. It runs alongside whichever route you take.

The market difference: an agency creates dependency, a system creates freedom. The agency ships fast, but the knowledge stays outside. When they leave, the knowledge leaves. I build a system that stays. I leave, the system stays.

What is the hardest proof that this works?

Concrete results from engagements, not slides. At Emporix I supported over 100 percent revenue growth through Pedalix; at Xorlab the ROI landed under two months. The track-record data points are checkable, not a pitch.

Three professionals plus ten agents replace teams that used to count 30 people because the agents run on a clean Context Engine and a human leads them. Apply the same logic to your product team and you exit the engineering jam without hiring 20 more devs. That's the variable that moves the outcome today.

What sticks

✅ What works. With three professionals and a team of agents you play big without hiring big. That is reality today, not a pitch.

❌ What doesn't work. Agents on an empty foundation. Without context and without leadership you get faster garbage, not results. Anyone who thinks AI does the work by magic gets disappointed.

⚠️ Warning. The bottleneck is not the tool. The bottleneck is you, if you let ten machines run without a plan. Leadership and context first, then the agents.

Thirty back then, three today. The reason is not that AI does everything. The reason is that execution went cheap and leadership plus context drive the outcome. If you sit at the intersection of product, GTM and AI and want out of "stuck in the middle", that's your lever.

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Frequently Asked Questions

What does Agentic GTM look like in practice?

A small team of professionals leads while three to five AI agents run the execution across marketing, sales and operations around the clock. The agents work on a shared Context Engine so they know your market, your offer and your CRM.

What is the Context Engine?

The Context Engine is the prepared business and code context of your company, readable by AI agents. It holds positioning, pipeline data, product knowledge and code understanding in one place. Without it, agents produce plausible smoke instead of useful work.

When is a company stuck in the middle?

After product-market fit, before scale. The product sells, but processes, data and team are not ready for the next jump. Typical between 20 and 200 employees in B2B software.

Does Agentic GTM replace the humans on the team?

No. It replaces execution volume, not leadership. The three professionals become more important, because they lead ten agents instead of ten sales reps. Whoever can lead wins.

How do I start with three instead of thirty?

First measure where the bottleneck really sits (positioning, pipeline, product), then build one central system, then put three to five agents on the biggest bottleneck. Learn it via gtm.science, have Pedalix build it for you. And the software to go with it via teklens.ai.