The guide covers 7 criteria for evaluating martech platforms. Here are three of the most important ones, because they’re also the most commonly misrepresented by vendors. Use them as a starting point for any evaluation conversation, and download the full guide for all seven.
EVALUATION GUIDE
Evaluating Agentic AI in MarTech: What to Look For and What to Question
Every MarTech vendor says they have “agentic AI” now. Most of them don’t really. This guide gives you the criteria, questions, and red flags to help you tell the difference, so you can make a confident call before you sign anything.
Here’s what’s inside:
- What Agentic AI Actually Means
The difference between predictive, generative, and agentic AI, and the four bars any platform must clear to qualify as genuinely agentic. - Are You Ready to Evaluate?
A pre-evaluation checklist covering data readiness, campaign volume, and what success looks like. - 7 Evaluation Criteria
A structured framework covering agent scope, data context, human-in-the-loop controls, feedback loops, and more. - Questions to Ask Every Vendor
What good answers look like, and which answers should make you nervous. - Red Flags to Watch For
The signals that tell you AI capabilities are bolted on, not built in.
What Good Agentic AI Actually Looks Like
Scope of Execution
A genuinely agentic platform handles segmentation, content, journey setup, and reporting from a single workspace. If it only touches one module, it's not agentic. It's a feature with good PR.
What good looks like: The agent can build audiences, draft templates, configure journeys, and pull performance reports from one conversational interface.
What good looks like: The agent can build audiences, draft templates, configure journeys, and pull performance reports from one conversational interface.
Platform-Native Context
A platform that doesn't know your customer schema, your existing segments, or your campaign history isn't agentic. It's autocomplete. Real agentic AI operates with full awareness of your actual data, not generic defaults.
What good looks like: The agent can reference your existing campaigns, journeys, and customer attributes without manual setup or external prompt engineering.
What good looks like: The agent can reference your existing campaigns, journeys, and customer attributes without manual setup or external prompt engineering.
Human-in-the-Loop Controls
Agentic AI without approval workflows isn't a productivity tool. It's a liability. Every output should require explicit human review before anything reaches a customer. If a vendor can't show you that, keep looking.
What good looks like: Every segment, template, and journey the agent builds is surfaced as a draft for marketer review. Nothing publishes automatically.
What good looks like: Every segment, template, and journey the agent builds is surfaced as a draft for marketer review. Nothing publishes automatically.