Proactive agents change how products talk to users. Instead of waiting for a question, they speak first and push information, offers, or reminders. Core challenge is not what to say but when to interrupt, which channel to use, and whether message is worth user’s attention. If agents interrupt too often users mute them; if they wait too long the opportunity disappears or the problem worsens. Every message is a bet: send only when its expected value exceeds cost of interruption.
Value can be broken into four parts: stake at risk, likelihood user will act, how fast opportunity expires, and who benefits—the user or the platform. High‑value, time‑sensitive messages go now via voice or SMS. Moderate value can wait for a digest or an in‑app card. Low‑value messages should never be sent.
To make calls, teams already have tools. Uplift models estimate whether a nudge will cause desired action. Contextual bandits learn best moment and channel for each individual user. Receptivity models predict whether user is able to engage right now—driving, in a meeting, or free. These models need labels, which come from tracking whether users actually acted on notification.
A newer class of decision models fits job perfectly. Systems such as TypeSafe’s Jev or open‑weights Julia 1 from Supersonic Labs return only a typed judgment—a choice, a score, or a yes/no—with a probability. They run on a CPU in a few tens of milliseconds, so an agent can evaluate thousands of triggers cheaply before invoking large language model to craft message. Decision model answers: Is this worth an interruption? How urgent? Send now, wait, batch, or drop? Which channel—card, chat, SMS, or voice—matches value?
Limits are clear: these models only judge context they are given, so they will not learn hidden habits like a user who ignores mornings unless that signal is fed in. Feedback and retraining keep them accurate.
When building proactive agent, start by defining value formula for your use case, collect outcome data, train uplift, bandit, and receptivity models, then wrap them with lightweight decision model that chooses timing and channel. Measure impact on user action and satisfaction, and iterate. Winner will be team that speaks rarely, at the right moment, in the right place, and always on the user’s side.
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