AI media

AI media inverts how advertising works. In traditional programmatic, the platform sends a bid request out — a thin signal containing a page URL, device type, and maybe a user ID — to a remote decision-maker that lacks the context to make a great ad. The decision-maker doesn’t have the context. The context-holder doesn’t make the decision. AI platforms reverse this. Instead of sending signals out, they receive everything in: product catalogs via sync_catalogs, conversion events via sync_event_sources, brand identity via brand.json, content standards via governance policies, and optimization goals on every media buy. The platform’s LLM then has full context — the user’s conversation, the brand’s catalog, suitability rules, and what success looks like — to generate the right ad in the right moment. The decision-maker is the context-holder. That is the fundamental shift.

New to AdCP? Start with the introduction for a protocol overview, or the seller integration guide for the general steps to expose inventory to AI buyer agents. This guide covers AI media-specific product modeling, workflows, and measurement.

For buyers and brands

For a non-technical guide to AI media buying — what it requires, what data you provide, and how to get started as a brand, agency, or small business — see the buyer’s guide to AI media. The rest of this page covers technical product modeling, workflows, and measurement for teams building or evaluating integrations. AdCP’s sponsored_intelligence channel is designed for this reversed data flow. Rather than defining bid request formats, it defines the data buyers push into platforms: catalogs, events, brand standards, and goals. The platform uses this to generate sponsored responses, AI search results, generative display, and brand experience handoffs via Sponsored Intelligence.

What buyers push in

Data	AdCP mechanism	What it enables
Product catalog	`sync_catalogs` with `type: "product"`	Platform generates ads from real product data — titles, prices, images, attributes — and matches user intent to inventory
Offering catalog	`sync_catalogs` with `type: "offering"`	Services, promotions, and seasonal campaigns enable commerce handoffs via SI
Conversion events	`sync_event_sources`	Platform optimizes toward real business outcomes, not proxy metrics
Brand identity	`brand.json`	Voice, visual guidelines, and positioning inform generated creative
Content standards	Governance policies	Suitability rules the platform enforces at decision time
Success definition	`optimization_goals` on packages	Platform knows what “good” means for this campaign

The more data the platform has, the better the ad. Product catalogs feed creative generation. Offering catalogs enable commerce handoffs. Conversion events close the optimization loop. Brand identity ensures the output sounds right. Content standards ensure it stays safe. None of these are administrative setup — together, they are the ad. AI media sellers come in two shapes: first-party AI platforms (a single AI assistant or search engine selling its own inventory) and AI ad networks (aggregating inventory across multiple AI platforms and publishers). The account model and publisher properties differ accordingly — a first-party platform is a walled garden with explicit accounts, while an ad network may use implicit accounts and represent multiple publisher properties.

How AI media differs from programmatic

Traditional programmatic	AI media	Why it matters
Platform sends bid request out with limited context	Buyer pushes catalogs, events, standards in via AdCP	The decision-maker has full context
Remote DSP selects ad without conversation context	Platform LLM generates ad with user conversation + brand data	Ads are contextually relevant by construction
Pre-built creative uploaded by buyer	Creative generated at serve time from synced catalog	No creative production bottleneck
Targeting = audience segments, geo, demographics	Targeting = conversational relevance + keyword intent	Natural alignment with user needs
Optimization = bid adjustments on proxy metrics	Optimization = `optimization_goals` with conversion events	Platform optimizes on real outcomes
Brand safety = blocklists and URL classification	Content standards = governance policies enforced at generation time	Suitability is built in, not bolted on
Measurement = impressions, clicks, CTR, viewability	Measurement = engagements, clicks, cost per engagement	Metrics match the medium

AdCP mapping

AI media concept	AdCP equivalent	Reference
AI platform / AI ad network	Sales agent (MCP server)	`adagents.json`
Advertiser account with platform	`account_id` + `list_accounts`	Accounts & Agents
Pushing brand data into platform	`sync_catalogs`, `sync_event_sources`, `brand.json`	Catalogs, Brand identity
Content standards and suitability	Governance policies	Campaign governance
Sponsored response / recommendation	Product with generative native format	Media Products
AI search sponsored result	Product with native format	Media Products
Generative creative from brand context	`build_creative` with brand identity	Generative Creative
Brand experience handoff	Sponsored Intelligence protocol	SI Specification
Defining success	`optimization_goals` on packages	Optimization & Reporting

Each seller is a separate sales agent — whether that’s a first-party AI platform or an ad network aggregating across multiple platforms. Their ad offerings are modeled as products. The buyer pushes in the data the platform needs to make great ads: product catalogs, brand identity, conversion events, content standards, and optimization goals. Account relationships depend on the seller type: first-party platforms typically use explicit accounts (require_operator_auth: true), while ad networks may use implicit accounts where the agent is trusted to declare brands via sync_accounts.

Why an open standard?

If you’re building an AI platform’s ad infrastructure, you have three options for plugging in demand:

Approach	How it works	Data flow	Scaling story
Proprietary API	Build a custom REST API. Each buyer integrates individually.	Data flows in (good), but every buyer needs a custom integration	Linear — each new buyer = new integration work on both sides
Traditional SSP	Integrate with prebid/GAM. SSP sends bid requests to DSPs.	Data flows out — thin signals (URL, device, user ID) go to remote bidders that lack conversation context	Good reach, wrong model — the platform gives up the decision to a remote system
AdCP (open standard)	Implement AdCP tasks over MCP or A2A. Any compliant buyer agent can plug in.	Data flows in — catalogs, events, standards, goals push into the platform via standard tasks	Network effect — every new buyer agent works automatically

The proprietary API gets the data flow right but doesn’t scale. The SSP scales but gets the data flow wrong. AdCP gets both right: data flows in through a standard interface that any buyer agent can use without a custom integration.

AdCP extends your platform, not replaces it

AdCP is not an aggregation layer that sits on top of your ad stack. It’s a standard interface to it. You implement AdCP tasks inside your platform — get_products queries your inventory system, sync_catalogs writes to your catalog store, create_media_buy creates campaigns in your ad server. The tasks are thin wrappers around capabilities you’re already building. The accounts protocol is where this becomes concrete. When a buyer calls sync_accounts, your platform creates a real advertiser account — the same kind of account a human would create through your dashboard. When they call list_accounts, they see the same accounts. sync_catalogs writes to the same catalog store your ad serving reads from. sync_event_sources registers the same conversion events your optimization engine uses. This means:

Your existing dashboard and API keep working — AdCP is an additional interface to the same backend, not a replacement
Accounts created via AdCP are real accounts — they show up in your admin tools, your billing system, your reporting
Catalog data synced via AdCP feeds the same serving pipeline — there’s no separate “AdCP inventory” and “direct inventory”
Buyer agents get the same capabilities humans get — same products, same pricing, same measurement

AdCP works over two transport protocols: MCP (direct integration with AI agents) and A2A (agent-to-agent workflows). The same task definitions work over both. See the protocol comparison for when to use which.

Ad networks

AI ad networks aggregate inventory across multiple AI platforms into a single seller interface. The protocol supports this topology natively — networks use account chains, catalog forwarding, and unified delivery reporting to sit between buyer agents and underlying platforms. For the full details on product modeling, governance, and Sponsored Intelligence routing through networks, see the AI ad networks guide.

The product spectrum

AI media sellers offer several distinct product types, each with different creative generation patterns, pricing models, and measurement capabilities.

AI search sponsored results

Sponsored results within AI-powered search experiences. Similar to traditional search ads but rendered within an AI’s synthesized response. Keyword targeting is the primary relevance signal.

{
  "product_id": "ai_search_sponsored",
  "name": "Sponsored results - AI search",
  "description": "Sponsored results appearing within AI search responses. Targeted by keyword relevance to user queries.",
  "channels": ["sponsored_intelligence"],
  "publisher_properties": [
    { "publisher_domain": "ai-platform.example.com", "selection_type": "all" }
  ],
  "format_ids": [
    { "agent_url": "https://ads.ai-platform.example.com", "id": "search_result_native" }
  ],
  "delivery_type": "non_guaranteed",
  "pricing_options": [
    {
      "pricing_option_id": "search_cpc",
      "pricing_model": "cpc",
      "floor_price": 0.75,
      "price_guidance": { "p50": 2.50, "p75": 5.00 },
      "currency": "USD",
      "min_spend_per_package": 500
    }
  ],
  "delivery_measurement": {
    "provider": "Platform analytics",
    "notes": "Click tracking on sponsored result links. Query-level reporting available."
  },
  "metric_optimization": {
    "supported_metrics": ["clicks"],
    "supported_targets": ["cost_per"]
  },
  "creative_policy": {
    "co_branding": "none",
    "landing_page": "any",
    "templates_available": true
  }
}

AI search products typically support keyword targeting. Sellers declare this in get_adcp_capabilities under media_buy.execution.targeting.keyword_targets, and buyers provide keywords on packages via targeting_overlay.

Generative display and video

Display and video ads generated from brand assets for placement within AI experiences — sidebars, interstitials between conversation turns, or visual responses. The platform uses build_creative to generate visual creative from the brand’s assets and guidelines.

{
  "product_id": "ai_generative_display",
  "name": "Generative display - AI experience",
  "description": "Display ads generated from brand assets, placed within AI experience surfaces.",
  "channels": ["sponsored_intelligence"],
  "publisher_properties": [
    { "publisher_domain": "ai-platform.example.com", "selection_type": "all" }
  ],
  "format_ids": [
    { "agent_url": "https://creative.adcontextprotocol.org", "id": "display_300x250" },
    { "agent_url": "https://creative.adcontextprotocol.org", "id": "display_native" }
  ],
  "delivery_type": "non_guaranteed",
  "pricing_options": [
    {
      "pricing_option_id": "display_cpm",
      "pricing_model": "cpm",
      "floor_price": 6.00,
      "currency": "USD",
      "min_spend_per_package": 1000
    }
  ],
  "delivery_measurement": {
    "provider": "Platform ad server",
    "notes": "Impressions per IAB guidelines."
  },
  "creative_policy": {
    "co_branding": "none",
    "landing_page": "any",
    "templates_available": false
  }
}

Unlike sponsored responses, generative display uses standard format IDs from the creative agent. Buyers provide creative via build_creative or inline on the media buy — the platform doesn’t generate creative from catalog data.

Brand experience handoffs (Sponsored Intelligence)

When a user shows high purchase intent, the AI platform can hand off the conversation to the brand’s own AI agent via the Sponsored Intelligence protocol. This enables rich, multi-turn brand experiences — product recommendations, comparisons, configuration, and even transaction completion — without leaving the AI assistant. Brand experience handoffs are unique to AI media. The brand syncs offerings via sync_catalogs with type: "offering", and the platform triggers handoffs when user intent aligns with an offering. See the SI specification for the session lifecycle and capability model.

End-to-end workflow

Measurement

AI media measurement centers on engagement and intent signals rather than traditional impression-based metrics.

Key metrics

Metric	Field	Description
Engagements	`engagements`	User interactions with sponsored content (clicks, expansions, follow-up questions)
Clicks	`clicks`	Outbound clicks to advertiser destination
Cost per click	`rate` (CPC)	Average cost per click
Cost per engagement	Derived from `spend / engagements`	Average cost per engagement interaction

Conversion tracking

AI platforms that support conversion tracking declare it in get_adcp_capabilities under conversion_tracking. Buyers set up event sources via sync_event_sources and use kind: "event" optimization goals on packages — the same pattern as any other channel. See Optimization & Reporting for details.

Beyond ads: commerce in the conversation

AI surfaces are uniquely positioned for commerce. A user asking an AI assistant about running shoes is expressing intent in a context where the platform can recommend, compare, and — eventually — help the user buy. Today, AdCP handles the advertising layer: catalogs, media buys, and delivery. Offering catalogs via sync_catalogs already enable commerce handoffs through Sponsored Intelligence sessions, where the user can browse products and transition to checkout. As commerce protocols mature, the path from “interested” to “purchased” will happen entirely within the conversation.

Best practices

For buyers

With AI media, the quality of what you push in determines the quality of what comes out. Your catalogs, conversion events, brand identity, and content standards are not administrative setup — they are the inputs the platform uses to generate the right ad, match it to the right moment, and help the user transact.

Push rich catalogs — products and offerings — sync via sync_catalogs with detailed descriptions, multiple images, structured attributes (size, color, category), and accurate pricing. Product catalogs feed creative generation. Offering catalogs enable promotions and commerce handoffs. The platform generates ads and matches intent from this data — thin feeds mean thin ads.
Push conversion events — sync event sources via sync_event_sources so the platform can optimize toward your actual business outcomes. AI platforms can close the loop between conversational context and conversion — give them the data to do it.
Push brand identity — brand.json provides voice, visual guidelines, and positioning. The platform uses this to generate creative that sounds and looks like your brand, not generic sponsored content.
Push content standards — use governance policies to define suitability rules. Because the platform generates creative at serve time, these rules are enforced during generation, not applied as post-hoc filters.
Set clear optimization goals — use optimization_goals on every package to tell the platform what success means. Combine with conversion events for outcome-based optimization.
Set up accounts early — check get_adcp_capabilities for the account model. First-party platforms require OAuth; ad networks may accept sync_accounts directly.
Use sandbox first — validate the full data sync pipeline (catalogs, events, brand identity, standards) before committing real spend.
Use keyword targeting for AI search — specify relevant keywords on AI search packages to reach high-intent queries.

For sellers

The core value of AI media is that buyers push everything the platform needs to make great ad decisions. Your job as a seller is to accept that data and use it well.

Accept catalogs — products and offerings — implement sync_catalogs for both product and offering types. Product catalogs feed creative generation. Offering catalogs enable promotions, services, and commerce handoffs. Declare catalog_types on products so buyers know what to sync. The more catalog data you ingest, the better you can match user intent to advertiser inventory.
Accept conversion events — implement sync_event_sources so buyers can share what success looks like. This lets your platform optimize toward real business outcomes, not proxy metrics. The combination of conversational context + conversion data is what makes AI media optimization superior to remote bid optimization.
Accept and enforce content standards — support governance policies so buyers can push suitability rules into your platform. Because you generate creative at serve time, you can enforce these rules at generation time rather than filtering after the fact.
Honor optimization goals — buyers set optimization_goals on packages to tell you what “good” means. Use these alongside conversion events and catalog data to close the optimization loop.
Implement accounts — first-party platforms should use require_operator_auth: true (each advertiser authenticates independently). Ad networks aggregating across publishers may use implicit accounts instead.
Support sandbox — declare account.sandbox: true in capabilities. Buyers need to validate the full data sync pipeline (catalogs, events, standards) before committing real spend.
Report engagement metrics — include clicks and engagement data in delivery reporting so buyers can evaluate AI media alongside other channels.
Set channels: ["sponsored_intelligence"] on all products so buyers can filter by channel.
Support keyword targeting for AI search products — declare keyword_targets in capabilities and honor targeting_overlay.keyword_targets on packages.

Account state — How catalogs, event sources, and campaigns build on the account
Accounts & Agents — Account setup and billing relationships
Media Channel Taxonomy — sponsored_intelligence channel definition
Media Products — Product model reference
Catalogs — Product, offering, and other catalog feeds
Brand identity — Product catalog and brand identity
Generative Creative — AI-powered creative generation
Sponsored Intelligence — Brand experience handoffs in AI assistants
Sandbox mode — Testing with sandbox accounts
Optimization & Reporting — optimization_goals and metric optimization
Delivery Reporting — Delivery metrics

Getting Started

Building with AdCP

Protocol

FAQ

Trust & Security

Reference

Certification

AI media

AI media

For buyers and brands

What buyers push in

How AI media differs from programmatic

AdCP mapping

Why an open standard?

AdCP extends your platform, not replaces it

Ad networks

The product spectrum

Sponsored responses in AI assistants

AI search sponsored results

Generative display and video

Brand experience handoffs (Sponsored Intelligence)

End-to-end workflow

Measurement

Key metrics

Conversion tracking

Beyond ads: commerce in the conversation

Best practices

For buyers

For sellers

​AI media

​For buyers and brands

​What buyers push in

​How AI media differs from programmatic

​AdCP mapping

​Why an open standard?

​AdCP extends your platform, not replaces it

​Ad networks

​The product spectrum

​Sponsored responses in AI assistants

​AI search sponsored results

​Generative display and video

​Brand experience handoffs (Sponsored Intelligence)

​End-to-end workflow

​Measurement

​Key metrics

​Conversion tracking

​Beyond ads: commerce in the conversation

​Best practices

​For buyers

​For sellers

​Related documentation

AI media

For buyers and brands

What buyers push in

How AI media differs from programmatic

AdCP mapping

Why an open standard?

AdCP extends your platform, not replaces it

Ad networks

The product spectrum

Sponsored responses in AI assistants

AI search sponsored results

Generative display and video

Brand experience handoffs (Sponsored Intelligence)

End-to-end workflow

Measurement

Key metrics

Conversion tracking

Beyond ads: commerce in the conversation

Best practices

For buyers

For sellers

Related documentation