The Platform for Targeting Agents, Not Eyeballs

The Problem

Digital advertising is at risk.

As users increasingly rely on AI agents to fulfill tasks—like booking rides, ordering food, or finding products—they stop seeing or clicking traditional ads. This shift threatens the core revenue engine of the internet: eyeball-driven advertising.

But assistants still make choices. The opportunity lies in shifting ad spend from targeting users to targeting agents—the new interface through which decisions are made.

Without a new framework, brands lose visibility, services are commoditized, and user preferences are ignored.

The Insight

The agent is the new interface—but today’s ad systems aren’t built for agent-mediated decisions. Brands and service providers need a new way to reach users through the choices their agents make.

The Solution

AgentReach is the first real-time bidding and offer engine built on MCP — the Model Context Protocol.

MCP provides the infrastructure for structured, context-rich communication between services and AI agents. AgentReach monetizes that channel by enabling services to bid for fulfilling user intents.

Agents select offers based on user preferences—cost, speed, brand, loyalty—and explain their decisions transparently.

Inspired by real-time bidding (RTB) for ads, but reimagined for agentic commerce, AgentReach routes ad dollars toward actual decision points, not passive impressions.

How It Works

User asks their Agent to: “Get me a ride to the airport”
User has previously request said “save me as much as possible”
An Intent_Request is issued to AgentReach which distributes the request to all registered services
Those services respond with real-time offers (discounts, faster arrival, loyalty perks)
Agent picks the best match based on the user’s preference model & finalizes the transaction via MCP with the
AgentReach logs and monetizes the transaction

Key Components

MCP Protocol Layer: Standard for agent-service context exchange
AgentReach Bidding Engine: Real-time offer competition
User Preference System: Tunable, portable profiles
Explainability Layer: Agents justify choices clearly to users
A parallel models for Android and iOS using those systems built-in intent models and evolving Agentic API’s

Why Now

Proliferation of AI agents across platforms (ChatGPT, Gemini, Alexa, etc.).
Decline in traditional ad engagement.
Demand from brands to preserve visibility and differentiation in an AI-first world.

Business Model

Take rate on fulfilled transactions.
Sponsored offers and performance-based promotions
Paid placements and premium offers
MCP orchestration licensing to platforms and assistants

Go-to-Market

Early partnerships with leading AI agents and assistant platforms.
Launch vertical pilots in ride-hailing, food delivery, and local services.
Expand to broader app ecosystem via an Android-compatible agent interface (e.g., intents + bidding + user preference layer).

Team

Experienced AI, mobile, and platform builders with deep roots in Android, mobile ecosystems, and monetization infrastructure.