Before your AI speaks first, decide what deserves an interruption

The next AI problem may be when to speak

An assistant that waits for a question has an obvious starting signal. An assistant working toward a goal in the background has to decide when it knows enough to speak first.

In a November 2 essay, LangChain cofounder Harrison Chase describes proactive, always-on agents as a new source of context-engineering challenges. He highlights notification calibration, memory selection, asynchronous work, long-term goals, and other unresolved issues. This is an engineering perspective, not a controlled finding that one notification strategy works for everyone.

The interesting practical question is narrower than building an assistant that manages your life. If a system watches for something useful, what should make it interrupt you?

Our proposed answer begins with an attention policy. It says which events deserve immediate delivery, which belong in a digest, which should remain available on request, and which should be ignored. You can develop the policy with fictional events before connecting any account or collecting anyone’s conversations.

That gives the assistant something more useful than a vague instruction to be proactive. It also gives you a way to notice when activity has become noise.

Source: Harrison Chase: New context engineering challenges in late 2026 ↗ · W3C WAI: Alert Pattern and interruption considerations ↗ · Amazon: Bee Frontier and Bee Eclipse announcement and feature limits ↗ · Bee: Frontier controls and mobile app availability ↗ · Bee: Free and Pro feature comparison ↗ · Apple App Store: Bee app requirements ↗ · Bee: Privacy Notice ↗

Choose a goal and give events a destination

Consider an assistant helping a student organization prepare a small workshop. Its job is to track the room reservation and the organizer’s remaining preparation decisions. This is an invented example, designed to make the tradeoffs concrete.

Write the goal as an outcome with boundaries. The room should be confirmed, the equipment question resolved, and the organizer informed of a material change before it affects the event. The assistant should not contact attendees, spend money, or change the reservation unless those actions are separately authorized.

Now name what the system can actually observe. Perhaps it can read a designated reservation thread and a shared checklist. It should not assume it sees every private conversation among the organizers. A clear observation boundary prevents a confident claim that no one has responded when someone replied elsewhere.

Define when the goal ends. After the workshop, the assistant no longer needs to keep reporting that the room is booked. If the event is canceled, the goal changes immediately even if older notes still contain the original plan.

This end condition is easy to overlook because ongoing assistance sounds useful by default. Yet a goal without a stopping rule can keep producing reminders after the underlying need disappears.

Start small enough that you can explain why each observed event matters. A general instruction to improve my productivity offers very little basis for judging whether any particular interruption is warranted.

For the workshop example, an immediate interruption could be a newly reported room cancellation close to the event. A routine confirmation might belong in an end-of-day digest. A minor checklist edit could be logged for later review. An unchanged reservation page might warrant no update at all.

These are proposed classifications, not universal urgency rules. The organizer should choose them according to their own situation. The same cancellation weeks ahead may deserve a different delivery path than one shortly before attendees arrive.

The four destinations help separate noticing from notifying. An agent can record a useful observation without demanding attention. It can also assemble several related observations into one message instead of sending them as separate discoveries.

For each destination, write a reason a person could understand. Immediate delivery might be justified because there is a real decision and a limited opportunity to make it. Digest delivery might be justified because the information is useful but does not change the next few hours.

Avoid an unrestricted important label. If importance has no operational definition, the system may treat every new detail as a reason to speak. Ask what the person could do differently after reading the message.

Also include an unknown state during design. If the agent cannot establish whether an event is current or relevant, it may need to investigate or report a narrow uncertainty. It should not silently translate uncertainty into urgency.

Make the message show why it arrived

An interruption should contain enough context to justify itself. For the workshop, a useful message might identify the changed room status, the source of that status, the time it was observed, and the decision now needed.

Do not make the person open a long thread just to learn why the assistant contacted them. At the same time, leave a route to the underlying evidence. A short claim with a traceable source is easier to assess than either an unexplained alarm or a complete transcript.

Distinguish observed change from inferred consequence. The venue’s message might say the projector is unavailable. The assistant may infer that a slide-heavy presentation needs an alternative. The message should make that inference understandable instead of presenting it as a second reported fact.

State what the assistant has already done. If it only read the message, it should not imply that a replacement is arranged. If it prepared a draft inquiry, say that the draft is ready rather than suggesting someone has been contacted.

A useful interruption can end with one next decision. It should not expand every issue into a new list of optional projects. When the person is already switching attention, clarity about the next action matters more than demonstrating everything the system can think of.

This is also a useful review criterion. If the proposed message does not reveal the reason for delivery, the attention policy may be too vague.

Group related events and respond to changed goals

Suppose the venue sends a cancellation, follows up with a clarification, and then offers an alternative room. Three separate urgent alerts may leave the organizer less informed than one updated account of the situation.

Give related observations a shared event identity in your design. The system can then recognize that a later message changes the same reservation issue rather than creating a new independent crisis.

Decide what warrants another interruption. A material change in available choices may justify one. A spelling correction in the venue’s note probably does not. The policy should describe the difference in terms of the user’s decision.

Be careful with repeated delivery after acknowledgment. If the organizer has already seen the issue and is working on it, a reminder needs a new reason. The mere fact that the issue remains open is not always sufficient.

This does not mean silence after the first alert. A deadline approaching without a response may matter, if the organizer has chosen that rule. Make the condition explicit so the system can explain why it spoke again.

For a paper simulation, use several fictional messages about the same reservation and ask which should merge, which should update a digest, and which should prompt a new alert. You will often discover that event grouping is essential before any sophisticated model judgment is needed.

A background assistant may be halfway through an investigation when the user changes direction. For the workshop example, the organizer might decide to move online while the agent is still comparing alternative rooms.

The earlier work may remain accurate and become irrelevant at the same time. The agent needs to check whether its conclusion still serves the active goal before delivering it.

Design a simple goal revision record. It can identify the current goal, the change time, and the status of work started under an older version. You do not need a complex system to understand the principle: a result should carry enough context to establish which request it answers.

Decide how to handle late arrivals. A room comparison finished after the online decision may be worth saving without an interruption. If it reveals a cancellation fee that still affects the organizer, that part may remain relevant. The policy should allow selective relevance rather than automatically sending or discarding everything.

Cancellation also needs a visible state. If the agent cannot stop an external operation already underway, it should say what remains in progress and avoid promising that all effects were reversed.

Testing this with fictional events is especially valuable because it exercises responsiveness without touching real reservations. Change the goal midway through the sequence and inspect which queued notifications still make sense.

Collecting more context creates another decision

An agent may be able to observe messages, calendars, or ambient conversations. That does not mean every available source belongs in the project. Begin with the source necessary for the chosen goal, and add others only when you can explain their contribution.

For the workshop, a designated thread and checklist may be enough. Continuous audio capture would introduce people and material unrelated to the room decision. A typed note from the organizer is a no-purchase alternative when the relevant information came from an in-person conversation.

An optional hardware example is the Bee Frontier Onyx AI wearable (affiliate link): Bee’s documentation lists conversation summaries, a microphone button and indicator, and iOS and Android apps. Suggested to-dos and app integrations require Bee Pro; check your phone’s current app requirements and account setup before buying, obtain participants’ permission before capture, and review what you choose to retain.

As an Amazon Associate I earn from qualifying purchases. This optional recommendation is not a claim of hands-on testing.

That is a product example, not evidence that a wearable improves an agent’s judgment. Signal Scroll has not tested the device. The attention-policy exercise works without it.

Amazon says processed audio is deleted, while transcripts and summaries have their own controls. Do not interpret an audio-deletion statement as meaning no derived information remains. The relevant choice for a particular conversation includes what is captured, what is kept, and who has agreed to that use.

Keep the capture decision separate from notification delivery. A system that sends fewer messages may still collect more information than the task needs. Quiet operation is not, by itself, a privacy guarantee.

Design the interruption so people can still work

Delivery mechanics can undermine a sensible policy. An alert that disappears before it can be read or steals the keyboard focus may create a new problem while announcing the old one.

W3C’s Alert Pattern describes important messages that attract attention without moving keyboard focus. It also cautions about disappearing alerts and frequent interruptions, including their effects on people with visual or cognitive disabilities. Those interface considerations remain relevant when an AI decides which message to deliver.

For a prototype, let the person revisit notifications in a stable place. Provide an understandable way to mute a goal, change its delivery schedule, or stop it. The controls should be available when the person needs them, rather than hidden in the agent’s remembered preferences.

Avoid making the person justify every dismissal. A simple not useful response can be enough feedback for a first design. If the system adapts its policy, show what changed so the person can correct an overreaction.

Keep urgency visible in words. Color or sound may support the message, but the person should be able to understand its purpose from the text. Do not rely on a decorative badge to distinguish a routine digest from a time-sensitive decision.

Test the interface separately from the agent’s classification. A correct decision to notify can still be delivered poorly, and a beautiful notification can still contain an unjustified interruption. These are different responsibilities.

Rehearse the policy and measure usefulness

Write a short sequence of fictional events for the workshop. Include an ordinary confirmation, a duplicate message, a changed equipment condition, a canceled goal, and a result that arrives after the cancellation.

Ask the assistant to classify each event using the policy. Require a brief reason tied to the current goal, rather than an open-ended judgment about importance. Record where it asks for missing information.

Now change one condition. Move the event date, alter the organizer’s chosen delivery window, or declare that the equipment question has already been resolved elsewhere. The classifications should change only where the new condition is relevant.

Include a tempting but irrelevant observation, such as a venue offering another service. An assistant optimizing for helpful suggestions may try to turn it into a new task. The policy should let it ignore the information when it does not serve the goal.

Review disagreements before connecting anything. Some will expose a poor model decision. Others will reveal that the policy never specified what the organizer wanted. Fix the missing rule and repeat the particular case, rather than treating every disagreement as proof of a general failure.

This rehearsal produces a small evaluation set you understand. It is more useful for the first version than an elaborate dashboard full of activity counts whose relationship to attention remains unclear.

If you later run an authorized trial, count more than messages sent. Record which notifications enabled a decision, which repeated known information, which arrived too late, and which important events were missed.

Do not optimize only for a low interruption count. An agent that never speaks can look excellent on that measure while failing its purpose. Likewise, do not reward it for every reminder, because that creates an incentive to manufacture reasons to contact the person.

A small review can focus on individual cases. Pick one useful message and one unhelpful one. Ask what evidence and rule produced each result, then revise the policy where the difference reveals something actionable.

Let the person stop the trial easily. A proactive assistant should remain responsive to an explicit change of preference. The value of the system depends partly on whether its involvement stays welcome as circumstances change.

Chase’s essay is useful because it names these systems as an open engineering problem. You do not need to solve every part to make a better first assistant. Start with one goal, an observable set of events, and a reasoned agreement about when attention is worth asking for. The ability to speak first becomes useful when it comes with the judgment to wait.