An Introduction to Agentic GTM Engineering
This guide is designed to help founders, marketers, and sellers navigate a seismic shift in go-to-market - one in which traditional roles and tactics are giving way to systems and processes powered by AI.
AI will not replace your work tomorrow, yet at Archbee, our hypothesis is simple: the era of the marketing specialist is fading.
In its place, a new breed of professional is emerging - the GTM engineer.
Part marketer, part seller, part operations.
Why a new role?
Because AI now has the knowledge once trapped in the minds of specialists, the real value no longer lies in being specialized in one tactic or channel but in designing scalable systems that integrate strategy, data, and execution seamlessly.
This playbook is for anyone who recognizes the need to adapt. Inside, you'll find:
- Actionable agents: Proven approaches to building agentic systems.
- Real-world examples: Stories and use cases to spark ideas and inspire action.
By the time you finish reading this, you'll have a clear vision of what’s possible in this new era and practical tools to begin engineering your own go-to-market motion.
This isn’t just about keeping up—it’s about staying ahead.
And if you wonder how to optimize your career in the coming years, maybe follow Rick Rubin. Here's what he said about his job as a music producer:
If you still don't believe the hype, here's what Andrej Karpathy (Previously Director of AI @ Tesla, founding team @ OpenAI, CS231n/PhD @ Stanford) has to say about the future.
Projects like OpenAI’s Operator are to the digital world as Humanoid robots are to the physical world. One general setting (monitor keyboard and mouse, or human body) that can in principle gradually perform arbitrarily general tasks, via an I/O interface originally designed for humans. In both cases, it leads to a gradually mixed autonomy world, where humans become high-level supervisors of low-level automation. A bit like a driver monitoring the Autopilot. This will happen faster in digital world than in physical world because flipping bits is somewhere around 1000X less expensive than moving atoms. Though the market size and opportunity feels a lot bigger in physical world. We actually worked on this idea in very early OpenAI (see Universe and World of Bits projects), but it was incorrectly sequenced - LLMs had to happen first. Even now I am not 100% sure if it is ready. Multimodal (images, video, audio) just barely got integrated with LLMs last 1-2 years, often bolted on as adapters. Worse, we haven’t really been to the territory of very very long task horizons. E.g. videos are a huge amount of information and I’m not sure that we can expect to just stuff it all into context windows (current paradigm) and then expect it to also work. I could imagine a breakthrough or two needed here, as an example. People on my TL are saying 2025 is the year of agents. Personally I think 2025-2035 is the decade of agents. I feel a huge amount of work across the board to make it actually work. But it *should* work. Today, Operator can find you lunch on DoorDash or check a hotel etc, sometimes and maybe. Tomorrow, you’ll spin up organizations of Operators for long-running tasks of your choice (eg running a whole company). You could be a kind of CEO monitoring 10 of them at once, maybe dropping in to the trenches sometimes to unblock something. And things will get pretty interesting.


