get_media_buy_artifacts
Retrieve content artifacts from a media buy for validation. This is separate from get_media_buy_delivery which returns performance metrics - artifacts contain the actual content (text, images, video) where ads were placed.
Response time: < 5s (batch of 1,000 artifacts)
Data Flow
The buyer retrieves artifacts the seller has collected per the sampling configuration agreed at buy creation time. Sellers may also push artifacts via webhook — get_media_buy_artifacts is the pull-based alternative. The buyer forwards artifacts to the verification agent for validation.
Request
Schema: get-media-buy-artifacts-request.json
| Parameter | Type | Required | Description |
|---|
account | account-ref | No | Account reference. Pass { "account_id": "..." } or { "brand": {...}, "operator": "..." } if the seller supports implicit resolution. Only returns artifacts for media buys belonging to this account. When omitted, returns artifacts across all accessible accounts. |
media_buy_id | string | Yes | Media buy to get artifacts from |
package_ids | array | No | Filter to specific packages |
failures_only | boolean | No | Only return artifacts where the seller’s local model returned local_verdict: "fail" (see behavior with unevaluated records) |
time_range | object | No | Filter to specific time period |
pagination | object | No | Pagination parameters (see below) |
Sampling is configured at buy creation time, not at retrieval time. The sampling rate, method, and per-channel configuration are part of the media buy’s governance.content_standards agreement. get_media_buy_artifacts retrieves artifacts that the seller has already collected per that agreement. For push-based delivery, configure artifact_webhook in create_media_buy.
| Parameter | Type | Default | Description |
|---|
pagination.max_results | integer | 1000 | Maximum artifacts per page (1-10,000) |
pagination.cursor | string | - | Opaque cursor from a previous response |
Response
Schema: get-media-buy-artifacts-response.json
Success Response
{
"$schema": "/schemas/content-standards/get-media-buy-artifacts-response.json",
"media_buy_id": "mb_nike_reddit_q1",
"artifacts": [
{
"record_id": "imp_12345",
"timestamp": "2025-01-15T10:30:00Z",
"package_id": "pkg_feed_standard",
"artifact": {
"property_rid": "01916f3a-a1d3-7000-8000-000000000040",
"artifact_id": "r_fitness_abc123",
"assets": [
{"type": "text", "role": "title", "content": "Best protein sources for muscle building", "language": "en"},
{"type": "text", "role": "paragraph", "content": "Looking for recommendations on high-quality protein sources for recovery", "language": "en"},
{"type": "image", "url": "https://cdn.reddit.com/fitness-image.jpg", "alt_text": "Person lifting weights"}
]
},
"country": "US",
"channel": "social",
"brand_context": {"brand_id": "nike_global", "sku_id": "air_max_2025"},
"local_verdict": "pass"
},
{
"record_id": "imp_12346",
"timestamp": "2025-01-15T10:35:00Z",
"package_id": "pkg_feed_standard",
"artifact": {
"property_rid": "01916f3a-a1d3-7000-8000-000000000040",
"artifact_id": "r_news_politics_456",
"assets": [
{"type": "text", "role": "title", "content": "Election Results Analysis", "language": "en"},
{"type": "text", "role": "paragraph", "content": "The latest polling data shows a tight race between candidates", "language": "en"}
]
},
"country": "US",
"channel": "social",
"brand_context": {"brand_id": "nike_global", "sku_id": "air_max_2025"},
"local_verdict": "fail"
}
],
"collection_info": {
"total_deliveries": 100000,
"total_collected": 1000,
"returned_count": 1000,
"effective_rate": 0.01
},
"pagination": {
"cursor": "eyJvZmZzZXQiOjEwMDB9",
"has_more": true
}
}
Response Fields
| Field | Description |
|---|
artifacts | Array of delivery records with full artifact content |
artifacts[].country | ISO 3166-1 alpha-2 country code where delivery occurred |
artifacts[].channel | Channel type (display, olv, ctv, podcast, social, etc.) |
artifacts[].brand_context | Brand/SKU information for policy evaluation (schema TBD) |
artifacts[].local_verdict | Seller’s local model verdict (pass/fail/unevaluated) |
collection_info | Artifact collection statistics — what the seller collected per the buy agreement |
pagination | Cursor for fetching more results |
Use Cases
Validate Collected Artifacts
# Get artifacts from seller (sampling was configured at buy creation time)
artifacts_response = seller_agent.get_media_buy_artifacts(
media_buy_id="mb_nike_reddit_q1"
)
# Convert to validation records
records = [
{
"record_id": a["record_id"],
"timestamp": a["timestamp"],
"media_buy_id": artifacts_response["media_buy_id"],
"artifact": a["artifact"],
"country": a.get("country"),
"channel": a.get("channel"),
"brand_context": a.get("brand_context")
}
for a in artifacts_response["artifacts"]
]
# Validate against verification agent
validation = verification_agent.validate_content_delivery(
standards_id="nike_brand_safety",
records=records
)
# Check for drift between local and verified verdicts
for i, result in enumerate(validation["results"]):
local = artifacts_response["artifacts"][i]["local_verdict"]
verified = result["verdict"]
if local != verified:
print(f"Drift detected: {result['record_id']} - local={local}, verified={verified}")
Focus on Local Failures
# Get only artifacts that failed local evaluation
failures = seller_agent.get_media_buy_artifacts(
media_buy_id="mb_nike_reddit_q1",
failures_only=True,
pagination={"max_results": 100}
)
# Verify these were correctly flagged
validation = verification_agent.validate_content_delivery(
standards_id="nike_brand_safety",
records=[{"record_id": a["record_id"], "artifact": a["artifact"]}
for a in failures["artifacts"]]
)
# Check false positive rate
false_positives = sum(1 for r in validation["results"] if r["verdict"] == "pass")
print(f"False positive rate: {false_positives / len(failures['artifacts']):.1%}")
failures_only and Unevaluated Records
When a seller does not run a local evaluation model, all records have local_verdict: "unevaluated". In this case, failures_only returns an empty result set — there are no failures to return.
Governance agents receiving validation results where every local_verdict is "unevaluated" should treat this as no local enforcement. The validation still works — the verification agent evaluates the artifacts normally — but there is no drift comparison to perform. Buyers can check content_standards_detail.supports_local_evaluation in get_adcp_capabilities to know whether failures_only will be useful before creating a buy.
local_verdict | failures_only returns? | Drift comparison possible? |
|---|
fail | Yes | Yes |
pass | No | N/A (not in result set) |
unevaluated | No | No — omit failures_only to retrieve all collected artifacts |
Non-Web Artifact Examples
Podcast
{
"record_id": "imp_podcast_001",
"timestamp": "2025-02-10T08:00:00Z",
"package_id": "pkg_mid_roll",
"artifact": {
"property_id": {"type": "apple_podcast_id", "value": "1234567890"},
"artifact_id": "episode_42_segment_3",
"assets": [
{"type": "text", "role": "title", "content": "The Future of Running Shoes", "language": "en"},
{"type": "audio", "url": "https://cdn.example.com/secured/ep42_seg3.mp3", "transcript": "Today we're talking to Dr. Chen about biomechanics research and how it's changing shoe design for marathon runners...", "duration_ms": 480000}
],
"metadata": {
"json_ld": [{"@type": "PodcastEpisode", "episodeNumber": 42, "name": "The Future of Running Shoes"}]
}
},
"country": "US",
"channel": "podcast",
"brand_context": {"brand_id": "nike_global", "sku_id": "vaporfly_next"},
"local_verdict": "pass"
}
CTV
{
"record_id": "imp_ctv_001",
"timestamp": "2025-02-10T20:15:00Z",
"package_id": "pkg_premium_ctv",
"artifact": {
"property_id": {"type": "app_id", "value": "com.streamingservice.tv"},
"artifact_id": "show_running_s2e5_scene_14",
"assets": [
{"type": "text", "role": "title", "content": "Championship Race - Final Stretch", "language": "en"},
{"type": "video", "url": "https://cdn.streaming.example.com/secured/s2e5_scene14.mp4", "transcript": "The runners round the final corner as the crowd erupts. Commentary: 'And she's pulling ahead now, this is going to be close...'", "duration_ms": 120000}
]
},
"country": "US",
"channel": "ctv",
"brand_context": {"brand_id": "nike_global"},
"local_verdict": "pass"
}
AI-Generated Content
{
"record_id": "imp_ai_001",
"timestamp": "2025-02-10T14:22:00Z",
"package_id": "pkg_conversational",
"artifact": {
"property_id": {"type": "domain", "value": "chat.example.com"},
"artifact_id": "session_x7k9_turn_15",
"assets": [
{"type": "text", "role": "paragraph", "content": "Based on your training schedule, I'd recommend increasing your long run distance by 10% each week. Here's a 12-week half-marathon plan...", "language": "en"}
]
},
"country": "GB",
"channel": "display",
"brand_context": {"brand_id": "nike_global", "sku_id": "pegasus_41"},
"local_verdict": "unevaluated"
}
local_verdict: "unevaluated" because the content is ephemeral and the platform relies on post-delivery validation rather than a local model.
Delivery vs Artifacts
| Aspect | get_media_buy_delivery | get_media_buy_artifacts |
|---|
| Purpose | Performance reporting | Content validation |
| Data size | Small (metrics) | Large (full content) |
| Frequency | Regular reporting | Sampled validation |
| Contains | Impressions, clicks, spend | Text, images, video |
| Consumer | Buyer for optimization | Verification agent |
