TL;DR: AI agents are now filtering, summarizing, and acting on notifications before users ever see them. Your notification strategy needs to account for machine interpretation, not just human attention. That means structured data, clear intent signals, and machine-readable formats are no longer nice-to-haves.

In late 2024, Anthropic released the Model Context Protocol. It was a technical announcement that most product teams ignored. By mid-2025, MCP had become the connective tissue for a new category of software: AI agents that don't just answer questions but take actions on behalf of users.

Google followed with Agent2Agent (A2A). Agentic browsers like Perplexity's Comet and Opera Neon started treating the web as something to be navigated programmatically, not just displayed. Microsoft announced that 2026 would be the year multi-agent systems move into production. The Linux Foundation created the Agentic AI Foundation to establish standards.

This isn't speculation about a future state. It's the infrastructure that's already running.

And it changes everything about how your notifications get processed.

The new notification flow

Here's how notifications used to work: Your system fires an event. A message gets composed. It lands on a user's device. The user decides what to do with it.

Here's what's increasingly happening now: Your system fires an event. A message gets composed. It lands on a device where an AI agent evaluates it. The agent decides whether to surface it, summarize it, batch it with others, act on it automatically, or dismiss it entirely. The user may never see your original copy.

Apple Intelligence notification summaries were the consumer-facing preview of this shift. Users started seeing AI-generated interpretations of notifications instead of the actual content. The BBC complained publicly after Apple's AI fabricated headlines. Apple had to pause the feature for news apps.

But that was just one implementation by one company. The broader trend is AI intermediaries sitting between your messages and your users across every channel and context.

What AI agents actually do with notifications

When an AI agent processes a notification, it's trying to answer a few questions:

Is this actionable? Can the agent do something with this information, or is it purely informational? A shipping notification with a tracking number and delivery date is actionable. A vague "Your order is on its way!" is not.

Is this urgent? Should this interrupt whatever the user is doing, or can it wait? Agents are getting better at inferring urgency from context, but they're also getting more aggressive about batching low-priority messages.

Is this redundant? Has the user already received similar information? Agents can correlate across sources. If a user got a shipping email, saw the tracking update in the retailer's app, and now gets a push notification with the same info, the agent might suppress the third touchpoint.

Can this be summarized or grouped? Multiple notifications from the same source often get collapsed into a single summary. Your carefully crafted copy becomes "3 updates from [App Name]."

What's the user's likely intent? Based on past behavior, does this user engage with these notifications? Do they typically take action, or dismiss? Agents learn patterns and pre-filter accordingly.

Why this breaks traditional notification strategy

Most notification strategies optimize for one thing: getting the user to see and engage with the message. Open rates, click-through rates, conversion from notification to action.

Those metrics assume the user is the decision-maker. When an AI agent is the first filter, you're optimizing for the wrong audience.

Consider a transactional notification like: "Your payment of $49.99 was successful."

To a human, this is confirmation. Peace of mind. To an AI agent, this is:

• A financial transaction (category: payments)
• Amount: $49.99
• Status: successful
• Likely urgency: low (confirmation, not action required)
• Likely action: archive or batch with other receipts

The agent might decide this doesn't need to interrupt the user at all. It gets filed away, surfaced later in a daily digest, or simply logged.

Now imagine you wanted the user to take an action from that notification, like reviewing a purchase or updating payment preferences. If your notification doesn't signal that intent clearly, the agent won't know to prioritize it.

What this means for notification design

Structured data matters more than copy

For years, the advice has been to write compelling notification copy. Short, punchy, action-oriented. That still matters for the humans who see your messages. But it's increasingly insufficient.

AI agents don't read your copy the way humans do. They parse it. They look for structured signals: event types, entity references, temporal markers, action indicators.

A notification that says "Don't forget to complete your profile!" is human-readable but machine-ambiguous. Complete what? By when? What happens if they don't?

A notification with structured metadata gives an agent something to work with. Here's what an agent-readable payload might look like:

{
  "event_type": "profile_completion_reminder",
  "user_id": "user_12345",
  "completion_percentage": 60,
  "suggested_action": "add_payment_method",
  "urgency": "low",
  "deadline": null,
  "human_readable": {
    "title": "Complete your profile",
    "body": "Add a payment method to unlock all features."
  }
}

This doesn't mean you need to abandon good copy. It means your notification infrastructure needs to support both layers: the human-readable message and the machine-readable context.

Intent signals need to be explicit

Humans infer intent from context and tone. AI agents need it spelled out.

If a notification requires user action, that needs to be explicitly flagged. If it's purely informational, that should be clear too. If there's a time sensitivity, include the deadline in a parseable format, not just "soon" or "before it expires."

The notifications that will get surfaced reliably are the ones that clearly communicate:

• What happened (the event)
• What the user should do (the expected action)
• Why it matters (the consequence of action or inaction)
• When it matters (temporal bounds)

Channel selection becomes more complex

Different channels have different levels of AI agent intermediation. Email is heavily filtered by AI-powered spam and priority systems. Mobile push is increasingly processed by on-device AI. In-app notifications and notification centers are (for now) less mediated.

This changes the channel selection calculus. It's not just "which channel will the user see?" but "which channel gives me the most direct path to the user without AI reinterpretation?"

For high-priority transactional messages, channels with less AI intermediation may become more valuable. For lower-priority updates, you might lean into the AI summarization and design your notifications to group well.

Batching and throttling become defensive, not just polite

Historically, batching and throttling were about respecting user attention. Don't spam people. Consolidate updates. Be a good citizen.

Now there's a harder edge to it: if you don't batch intelligently, AI agents will do it for you, and probably worse. They'll collapse your carefully sequenced onboarding flow into "5 notifications from [App]." They'll group unrelated updates because they came from the same source.

Taking control of your own batching and digest logic means you control the narrative. You decide what gets grouped and how it's summarized, rather than leaving it to an AI that doesn't understand your product.

What to do about it

Audit your notification payloads

Look at what you're actually sending. Not the copy, but the full payload. What metadata are you including? Is there structured data that an agent could parse, or just a title and body?

Most notification systems support custom data fields. Start using them intentionally:

• Event type classification
• Urgency level
• Expected user action
• Time sensitivity
• Related entity IDs

Even if agents aren't parsing this data today, they will be. Building the habit now means you're ready.

Test with AI summarization in mind

Take your notifications and run them through an LLM with a prompt like: "Summarize this notification in one sentence. What action, if any, should the user take?"

If the summary loses critical information or misinterprets the intent, that's a preview of what AI agents will do with it. Revise until the machine interpretation matches your intent.

Build for graceful degradation

Assume that some percentage of your notifications will be summarized, batched, or filtered by systems you don't control. Design for that case:

• Make sure critical information survives summarization
• Don't rely on notification sequences where order matters
• Include fallback channels for truly critical messages

Invest in channels you control

Your in-app notification center, preference UI, and inbox are channels where you have more control over the experience. As external channels become more mediated by AI, owned channels become more valuable.

This is the opposite of the "meet users where they are" advice from the last decade. It's "bring users to channels where you can reach them directly."

The bottom line

Notifications have always been a negotiation between your product and your user's attention. Now there's a third party at the table: AI agents that decide what gets through and what doesn't.

The companies that adapt will design notifications for both audiences. Human-readable copy that resonates emotionally. Machine-readable structure that survives algorithmic interpretation. Clear intent signals that tell agents what matters and why.

The companies that don't will watch their carefully crafted messages get summarized into oblivion, batched into irrelevance, or filtered out entirely.

The infrastructure you use to send notifications needs to support this dual-audience reality. Structured payloads, intelligent batching, channel orchestration that accounts for AI intermediation. These aren't future requirements. They're table stakes for 2026.

Further reading

Industry research:

• Google Cloud's 2026 AI Agent Trends Report
• IBM: The trends that will shape AI and tech in 2026
• Linux Foundation: Agentic AI Foundation announcement

Protocols and standards:

• Model Context Protocol (MCP) on GitHub
• Agent2Agent (A2A) Protocol on GitHub
• Anthropic's MCP announcement

Courier resources:

• Courier MCP Server documentation
• Building customer journeys with Courier
• Workflow automations
• Push notifications vs in-app messages

Courier provides notification infrastructure that supports structured payloads, intelligent batching, and multi-channel orchestration. If you're rethinking your notification strategy for an AI-mediated world, request a demo.