Blog

Building the AI agents is not the hard part

Beth Yehaskel · August 14, 2026

Companies are rushing to figure out how to use AI inside their organizations, and one thing has become very clear. It is not building the AI agents that trips companies up. It is everything around them.

Deciding which agents are worth building, and in what order. Determining where a human still needs to make the call. Designing how any of it connects to the teams, the data, and the processes you already run. That is where the pilots stall, and it is where the money goes quiet.

The pattern, across every size of company

Ernesto Humpierres and I have been tracking how AI is, and is not, working inside companies from startups to large enterprises. The pattern is consistent. Using AI and building agents is not the hard part any more. Almost anyone can stand one up. Designing them into a business and a revenue engine is the hard part, because that is a systems problem and a people problem, not a technology problem.

An agent dropped onto a revenue engine with disconnected data, undefined handoffs, and nobody accountable for the outcome does not underperform. It speeds up the failure that was already there, and it does so confidently.

So we started Cognitive Edge

We are not looking to replace your teams or add software to your stack. The goal is to help your team understand the system as it runs today, decide which changes are worth making and in what order, design the foundation on the stack you already own, and then weave the automation together with your people in a way that makes the most of both. Your team owns the result. Our success is when things are running smoothly, the outcomes are strong, and you do not need us any more.

Between us, we have led GTM teams, advised them, and built a deep understanding of how a revenue system works and how to improve it. Ernesto brings engineering and architecture. I bring twenty-five years across every part of the revenue engine and years as an executive coach, which turns out to be the part that decides whether any of it gets adopted.

The questions worth asking before you build anything

Before we recommend automating any piece of work, it has to clear four questions. Is the data clean enough that a decision made on it is a real decision? Is the decision actually decidable, or does it depend on context that lives only in someone's head? Is there a defined action waiting on the other side? Is anyone accountable for the outcome once the action is taken?

Most candidates fail at least one. That is the point. The questions find the one that passes and say plainly what would have to change for the others. Where a candidate fails on data, that becomes a scoped fix. Where it fails on accountability, that is a leadership conversation, and it is the one most companies skip.

Where this leaves a leadership team

I have done many kinds of work in the last twenty-five years, and this stitches all of it together: the operating experience, the systems thinking, and the coaching. The work is helping companies accelerate and internalize a new way of working, with AI and people together, rather than bolting a tool onto a process nobody redesigned.

So the question for any leadership team is not which agent to build first. It is how you are going to embed AI into the fabric of your revenue engine, and who is going to own it when it is running.

For the technical architecture and build side of that work: cognitive-edge.ai.

← All posts