Agentic AI in Martech: delegation and guardrails

Identity Resolution: a practitioner's guide to who owns the match

Identity resolution is a set of business decisions about who counts as the same person. Five approaches, and what each one costs you in ownership.

In part 4 of the Agentic CDP series I made a claim and then moved on rather quickly.

Identity resolution stops being a data-team hygiene project and becomes a precondition for anything agentic being trusted with a budget.

That deserved more than the paragraph it got, so here is the longer version.

Your agentic CDP will optimize whatever you measure

Every agentic CDP will be sold on goals-based marketing. That assumes a goal an agent can chase and a signal fast enough to learn from. Most teams have neither.

Martech Therapy: CDP strategy and agentic AIMatthew Niederberger

Will AI close the gap in Martec's Law?

Martech ships faster than any team can absorb. Martec's Law says the gap only grows. Here is the case that AI might, conditionally, close part of it.

You already know your stack ships more than your team can keep up with. That is not the interesting part, and it is not why I have been chewing on this for months.

The interesting question is whether AI is about to help with that or make it worse. Not in the abstract, but for the specific job of staying current with everything your tools ship after you have signed.

Here is the perspective I keep reaching for. Scott Brinker named the shape of this more than a decade ago and called it Martec's Law, yes, without an 'h'

The Context Audit

A follow-up to the follow-up. My last piece explaining "what the context layers has to do" argued that customer state has to be Queryable, Addressable, Current, and Accountable if agents are going to act on it in real time. It ended pointing at a one-page audit and stopped there.

This is that audit.

The context layer's job is to make the customer state usable when an agent needs to act on it. Whether it's doing that job is not a question anyone can answer from an architecture diagram. Architecture diagrams describe

Diverted by design? What the CDP split means for the mid-market

The CDP market didn’t just split into platforms and agents. It quietly diverted the mid-market along the way. This piece looks at what changed, why many teams now hesitate, and how CEPs became the safer path for getting things done.

In last week's part 1, I described how Gartner finally named the split inside the CDP category in their Magic Quadrant for Customer Data Platforms 2026. Platformization on one side. Agentification on the other.

Two futures, both serious, both demanding.

That still leaves a simple question hanging:

Who is this actually built for?

Because when you read this year’s Magic Quadrant carefully, the most telling signal isn’t who moved up or down, which opens another can of worms. It’s the kind of organization the category quietly assumes as the norm.

Large teams. Strong data foundations. Time

The CDP split is official (and it’s not the one vendors were selling)

For years, CDPs have been stretched like the industry’s Spandex. Data foundation one moment, orchestration layer the next, now even the “context brain” for AI. In this year’s Magic Quadrant, Gartner finally names the split. And it changes how everything should be read.

Reading this year’s Gartner Magic Quadrant for Customer Data Platforms gave me a familiar feeling.

Not, not that of reading a short novel, with its 46 pages. Not excitement. Not disbelief. More the quiet recognition you get when something you’ve been circling for a while finally lands on paper. The conclusions didn’t come out of nowhere. They arrived right on time for the category.

For years now, CDPs have been asked to stretch in every possible direction. Think of them as the industry’s Spandex. One moment they’re a data foundation. Then an orchestration layer. Then

When AI replaces processes, not people

A conversation with Paul Meinshausen and Duarte Garrido about the mental models teams struggle with when adaptive systems replace rule-based workflows. Most marketers aren't ready for this shift yet.

I set up this conversation with Paul Meinshausen from Aampe and Duarte Garrido from DOJO AI because I've been watching the same pattern repeat across different companies. Teams bring in AI tools, get excited about automation, then hit a wall when they realize the technology isn't just doing their work faster. It's doing it differently. That difference requires a mental model most marketers don't have yet.

Watch the full episode here ☝

Paul's background is in cognitive science and decision-making under uncertainty. Duarte came up through big brand marketing before building tools for challenger brands

Posts tagged with Agentic AI