get_media_buy_artifacts

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 requests artifacts from the seller using the same media buy parameters. The seller returns content samples based on the agreed sampling rate. The buyer then validates these against the verification agent.

Request

Schema: get-media-buy-artifacts-request.json

Parameter	Type	Required	Description
account_id	string	No	Filter to a specific account. Only returns artifacts for media buys belonging to this account. When omitted, returns artifacts across all accessible accounts. Optional if the agent has a single account.
media_buy_id	string	Yes	Media buy to get artifacts from
package_ids	array	No	Filter to specific packages
sampling	object	No	Sampling parameters (defaults to media buy agreement)
time_range	object	No	Filter to specific time period
pagination	object	No	Pagination parameters (see below)

Sampling Options

{
  "sampling": {
    "rate": 0.25,
    "method": "random"
  }
}

Method	Description
random	Random sample across all deliveries
stratified	Sample proportionally across packages/properties
recent	Most recent deliveries first
failures_only	Only artifacts that failed local evaluation

Pagination

Uses higher limits than standard pagination because artifact result sets can be very large.

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_id": {"type": "domain", "value": "reddit.com"},
        "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_id": {"type": "domain", "value": "reddit.com"},
        "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"
    }
  ],
  "sampling_info": {
    "total_deliveries": 100000,
    "sampled_count": 1000,
    "effective_rate": 0.01,
    "method": "random"
  },
  "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, video, audio, social)
artifacts[].brand_context	Brand/SKU information for policy evaluation (schema TBD)
artifacts[].local_verdict	Seller’s local model verdict (pass/fail/unevaluated)
sampling_info	How the sample was generated
pagination	Cursor for fetching more results

Use Cases

Validate Sample Against Standards

# Get artifacts from seller
artifacts_response = seller_agent.get_media_buy_artifacts(
    media_buy_id="mb_nike_reddit_q1",
    sampling={"rate": 0.25, "method": "random"}
)

# 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",
    sampling={"method": "failures_only"},
    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%}")

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

Related Tasks

• validate_content_delivery - Validate the artifacts
• calibrate_content - Understand why artifacts pass/fail
• get_media_buy_delivery - Get performance metrics