Technology is enabling, but it's also constraining — your choice of technology requires tradeoffs. You limit yourself in some ways to multiply your efforts in others. Customer engagement tools are no different. Consider how “campaigns” are used:
As orchestration — specifying which users get which messages under what conditions. This helps scale communication by breaking a logistical problem into manageable parts.
As analysis — monitoring results tied to the campaign’s audience, timing, and content. But this bundles together overlapping factors and obscures insight.
There’s no inherent reason for campaigns — especially not ones doing both jobs. That’s a design choice with real consequences. When teams start using agentic communication, we often see campaign-shaped use cases like: “Find users who haven’t engaged with a product in 30+ days and nudge them.”
That’s why an agentic approach to customer engagement separates orchestration from analysis. Orchestration is about expanding opportunity — maximizing who could benefit. Analysis is about learning — letting you retrospectively explore what worked, for whom, and under what conditions. That decoupling unlocks broader impact without sacrificing rigor.
It’s hard to move beyond campaigns, not just because it’s unfamiliar, but because it’s simpler. Campaigns offer a tidy abstraction to cope with messy user behavior. But simplicity for you doesn’t mean value for your users. Agentic orchestration embraces the mess by letting agents manage some of the complexity. Adopting an agentic mindset means you have to learn to treat orchestration and analysis as separate concerns, so orchestration can be dynamic and complex, while retrospective analysis can remain clear and human-scale.
