ChatGPT Pulse Goes Proactive: Designing Agents That Act

The shift from chat to workflow

On September 25, 2025, OpenAI announced ChatGPT Pulse for Pro users on mobile. Pulse is a simple idea with big consequences: each day, ChatGPT does asynchronous research based on your chats, memory, and feedback, then surfaces a focused set of visual updates you can scan and act on. In other words, the assistant starts the conversation. OpenAI’s description of Pulse emphasizes daily cadence, personalization, and control. Read the official introduction in Introducing ChatGPT Pulse.

If July 17, 2025 was the day ChatGPT Agent arrived to connect reasoning to action, September 25, 2025 marks the day the assistant moves from waiting to working. Agent showed that ChatGPT could choose tools, browse, log in to sites with your permission, and complete tasks end to end. Pulse now brings a lightweight, recurring workflow into the default experience. Together they move the product from reactive chatbots to permissioned, proactive assistants.

The most important part is not the novelty of a daily brief. It is the design pattern that flips the loop: the model proposes, you dispose. That means teams shipping on this stack need clear contracts, consent flows, and guardrails that match the capabilities of a system that thinks and acts.

To ground this analysis, this article reviews the two anchor events, offers a developer and PM playbook for building proactive agents responsibly, and closes with near term opportunities and risks for consumer and enterprise apps.

Why Pulse matters now

OpenAI frames Pulse as personalized research delivered as topical cards that appear once a day. It leans on memory and chat history, and it can optionally reference connected apps like Calendar or email when you turn those on. The experience is designed for quick scanning, with the option to open a card for more detail or to turn it into a chat thread you can keep. This shift pairs with trends like the 1M-token context race, which changes what agents can safely do with more context.

Three design decisions stand out:

Daily cadence with a narrow window. This sets expectations that Pulse is helpful, not addictive, and that it works while you sleep so you can get back to your day. That cadence also pairs well with scheduled work in Agent.
Explicit memory and connectors. Pulse only provides the extra context you enable. Memory can be switched off, and connectors are opt in. This keeps consent front and center.
Mobile first footprint. Pulse is currently on iOS and Android for Pro users. That constraint nudges the format toward glanceable summaries and short feedback loops, not sprawling dashboards.

In short, Pulse is a product nudge toward a world where your assistant does work without being asked and then asks for feedback. That is the moment where authority, autonomy, and safety rules matter.

The Model Spec change that unlocks autonomy

On September 12, 2025, OpenAI updated the Model Spec to support agentic use cases. Two changes are especially relevant:

A clarified authority order: Root → System → Developer → User → Guideline.
New agentic principles: act only within an agreed scope of autonomy, and control and communicate side effects.

See the summary in Model Spec September updates. The practical effect is a crisper chain of command for proactive behavior and a usable checklist for when the assistant proposes to do work before you ask. Treat the authority order as your policy engine: developer ceilings on autonomy should override casual user prompts that attempt to expand it.

From reactive chat to permissioned action

ChatGPT Agent, announced on July 17, 2025, stitched together a visual browser, a text browser, a terminal, and direct API access with connectors like Gmail and GitHub. It could log in to sites via an on screen browser, show its steps, and let you take over or stop at any point. It could also schedule completed tasks to recur. That release established a pattern where you set a goal, the agent chooses tools and steps, and you remain in control.

Pulse now sits one layer above that pattern. Instead of you declaring a goal to start the session, the assistant proposes likely goals and next steps based on your history and feedback. The design responsibility shifts in subtle ways:

Your defaults matter. What the agent is allowed to propose or attempt flows from your system and developer instructions, not just user prompts.
Consent becomes ongoing. Pulse is not a single transaction, it is a continuous relationship that evolves through thumbs up and thumbs down and through curation requests like “focus on client briefings this week.”
Failure modes change. The biggest risk is not dramatic misconduct. It is quiet overreach, where the assistant consumes more data than needed, nudges you toward actions you did not intend, or wastes time with slightly wrong updates.

A developer and PM playbook for proactive agents

Use this blueprint to design, build, and review proactive experiences like Pulse or to add proactive patterns to your own product.

1) State the contract up front

Define an autonomy scope in plain language: what the agent may read, what it may draft, what it may send, and what it must never do without explicit approval.
Set budgets: timeboxed research windows, token limits, cost ceilings, and the maximum number of external actions per run.
Bind to purpose: tie data access and memory to specific goals, and drop context not relevant to the stated purpose.

Write this into your system and developer instructions so the hierarchy is enforceable by design.

2) Use progressive consent as the default

Start read only. Show summaries and diffs of what will change, then require a single allow for low risk actions and an explicit confirm for medium risk actions.
Split permissions by capability and by data source. Approving calendar read does not imply email send. Approving one workspace does not imply another.
Offer session only, one day, and persistent scopes. Expire long lived scopes by default.

3) Minimize data and bind it to purpose

Ask for the least privileges that still enable the task. Prefer scoped tokens to all access keys.
Keep a privacy budget. Track how much sensitive data an agent has pulled into context and cap it per run.
Respect deletion. If a Pulse card is not saved, treat its context as ephemeral and purge it on schedule.

4) Control side effects and prefer reversibility

Dry run first. Before acting, have the agent produce a preview of the intended change, the systems it will touch, and the predicted side effects.
Produce drafts when possible. Draft emails, calendar invites, or slides rather than sending or publishing immediately.
Support undo and rollback. Keep idempotence keys and change logs so an admin or user can revert.

5) Keep a human in the loop where it counts

Require review gates for anything with legal, financial, or reputational risk. Show diffs and rationales.
Escalate exceptions. If the agent is uncertain or the cost exceeds budget, stop and ask.
Provide a single big red stop button. Users and admins need an obvious kill switch for a running workflow.

6) Build reliability guardrails into the loop

Use timeouts, retry budgets, and circuit breakers to prevent runaways.
Checkpoints and heartbeats. Have the agent emit progress updates so you can pause or redirect mid run.
Sandbox tools. Run browser and terminal actions in constrained environments and scrub outputs that cross the boundary.
Test agent flows like you test APIs. Maintain golden tasks with known outcomes and continuously evaluate success, latency, and failure modes.

7) Be transparent by default

Show an activity feed with timestamps, tools used, data sources accessed, and costs incurred.
Provide citations and screenshots when summarizing external pages. Screenshots help when pages change.
Let users export or delete their activity and memory footprints, and expose admin audit logs for enterprises.

8) Integrate safety rules into product logic

Encode the authority order in your orchestration layer. System and developer rules should hard block unsafe requests and cannot be overridden by a user prompt.
Apply domain rules. In finance, refuse to trade. In health, refuse to diagnose. Offer safe alternatives like education or referrals.
Use content and data filters. Detect and mask sensitive data before it enters long term memory or external logs.

9) Sweat the UX details that earn trust

Write clearly about what the agent will do next and why. Avoid passive phrasing.
Avoid pushy defaults. Quiet hours by default, no surprise notifications, and obvious ways to tune what shows up.
When the agent is unsure, say so and ask. Offer two to three clarifying choices rather than free form.

10) Give developers ergonomics and guardrails

Define tools with strict schemas and validation. Fail closed if the agent produces an invalid call.
Stream intermediate events so you can render narration and allow interruption.
Ship with feature flags and progressive rollout. Start with internal users, then a small cohort, then widen based on measured trust signals.

Near term opportunities in consumer apps

Personal planning. Daily briefings that combine calendar context, travel plans, and key to dos with quick links to draft messages or create events. Keep it read only until the user taps to act.
Household admin. Proactive reminders to reorder essentials, renew documents, or prepare for upcoming school events. Require explicit confirmation for purchases and keep spend limits tight.
Learning and skills. A weekly pulse tailored to a course or goal, with spaced repetition and practice prompts that adapt based on engagement.
Travel and leisure. Smart nudges tied to real dates, like “your flight leaves at 8 am, plan to leave by 6 am, here are three breakfast options near your gate.” Always show sources and allow a quick yes or no on reservations.

These use cases succeed when the assistant does small, concrete work that saves minutes each day without taking unapproved actions.

Near term opportunities in enterprise apps

Sales and customer prep. An agent that compiles account briefs before meetings, drafts agendas, and suggests follow ups, sourced from CRM, email, and public news. Require manager approved templates for any outbound send.
Operations reporting. Scheduled agents that assemble weekly dashboards, annotate anomalies, and propose next steps for owners to approve.
Recruiting coordination. Automated scheduling proposals with suggested interview panels and calibrated question banks. Always keep humans approving final sends and offer rollbacks when schedules collide.
Incident response and IT. Agents that watch signals and propose containment steps with playbook links and dry run plans. Require explicit authorization for any production change.

Enterprises need strong administration: workspace policies, website blocklists for the agent’s browser, data residency choices, and detailed audit logs. Proactive features should honor those controls from the first design sketch. For deeper context, see how data-native enterprise agents are changing integration patterns.

Risks to watch and how to mitigate them

Quiet overreach. The assistant might read more than needed or suggest actions outside user intent. Mitigation: narrow purpose, minimal scopes, previews, and clear opt outs.
Hallucinated updates. Even good models misread context. Mitigation: require citations or screenshots for factual claims, and design one click “not helpful” feedback that shapes future pulses.
Consent drift. Scopes granted months ago may still be active. Mitigation: expirations, periodic reviews, and scoped tokens.
Cost creep. Proactive research runs add up. Mitigation: budgets per user and per workspace, and visible cost tallies in the activity feed.
Security threats. Proactive flows expand the attack surface. Mitigation: strict tool schemas, sandboxed browsers, and continuous monitoring. Read why agent hijacking is the risk for 2025 era agents.

How to phase your roadmap

Phase 1: Read only Pulse. Start with daily research and cards. Memory off by default. No external actions. Measure helpfulness and false positives.
Phase 2: Drafts with approvals. Introduce draft only actions for a small set of high value tasks like agendas or follow ups. Add previews and diffs.
Phase 3: Limited actions. Allow sending or scheduling within strict budgets and with explicit consent. Keep an easy undo within a time window.
Phase 4: Scheduled workflows. Let users set recurring jobs with clear cost and scope notices. Maintain a dashboard to pause or cancel.

Each phase should be gated by safety tests, telemetry thresholds, and support playbooks. Do not promote to the next phase without evidence that users understand and control what the agent is doing.

The moment assistants become coworkers

Pulse is a small daily feature with a big strategic weight. Agent showed that ChatGPT can act. The Model Spec update gave us a chain of command and principles for agent behavior. Pulse puts those ideas into a routine that users can live with. If you build on this stack, the job is clear: make consent obvious, make side effects reversible, and make usefulness measurable. Late 2025 is likely to be remembered as the moment personal AI agents moved from chat to workflow. The winners will be the teams that earn the most trust.