Attribution Engine
Models multi-touch attribution
Orchestrator Agent
Marketing Attribution Modeling
Marketingmedium Risk
Complexity 4/5
Track journeys across channels, attribute revenue to touchpoints, calculate ROI
Manual attribution is inaccurate, overvaluing last-touch and missing cross-channel impact. This agentic workflow tracks complete customer journeys across all channels, builds attribution models, calculates multi-touch ROI by channel, and provides data-driven budget recommendations.
8-15x
Typical ROI
6-8 weeks
Time to Value
Marketing
Department
Complexity
Agent Architecture
Agent Architecture
Orchestrator tracks journeys and models attribution.
Journey Tracker
Tracks customer journeys
- •Collect touchpoints
- •Build journeys
- •Analyze paths
Journey Tracker
Tracks customer journeys
Attribution Modeler
Builds attribution models
- •Calculate attribution
- •Compare models
- •Select best
Attribution Modeler
Builds attribution models
Budget Recommender
Recommends budget allocation
- •Calculate ROI
- •Optimize spend
- •Forecast results
Budget Recommender
Recommends budget allocation
Orchestrator Pattern Architecture
Workflow Steps
1
Track all touchpoints
2
Build customer journeys
3
Analyze attribution
4
Calculate ROI
5
Recommend budget
6
Report insights
Required Dependencies
CRMSalesforce, HubSpot, Pipedrive
Key Performance Indicators
Click any KPI to view detailed measurement guidance, formulas, and typical ranges.
Governance Controls
Centralized LoggingVisibility
HIGH
Centralized Logging
Capture all agent interactions (prompts, outputs, data sources accessed) in a central, searchable system
Complexity: medium
Agent RegistryVisibility
HIGH
Agent Registry
Central inventory of all agents with metadata: owner, purpose, data sources, risk level, users
Complexity: low
Prompt Injection TestingRisk
Prompt Injection Testing
Regularly test agents for vulnerabilities (jailbreaks, prompt injection, data exfiltration attempts)
Complexity: medium
These controls help ensure secure, compliant, and auditable AI operations. High-priority controls are critical for production deployment.
Identified AI Risks
Hallucinations
Hallucinations
AI generating false or fabricated information presented as fact
Stale Information
Stale Information
AI using outdated data that no longer reflects current reality
Source Attribution
Source Attribution
Inability to verify or cite the original sources of AI-generated information
Recording Consent
Recording Consent
Lack of proper consent for recording, storing, or processing user interactions
Third-Party Data Processors
Third-Party Data Processors
Risks associated with external vendors processing sensitive data
Data Leakage
Data Leakage
Unintentional exposure of sensitive data through model training or outputs
Prompt Injection
Prompt Injection
Malicious manipulation of AI behavior through crafted input prompts
These risks should be mitigated through proper governance controls and operational procedures.
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