# AttributionCop 9000

> https://whycantwehaveanagentforthis.com/result/attributioncop-9000-aipowered-attribution-trust

Problem: # AI-Powered Attribution Trust Platform ## Executive Summary Today, attribution reports from multiple providers (Innovid, Clarivoy, Mastercard) are presented independently. When data quality issues,…

## verdict

tier: ACTUALLY NOT BAD
difficulty: 7/10
savage_line: You're building a 'trust layer' for attribution data, which is like a fact-checker for professional liars.

## ship_it_anyway

differentiator: Build the 'named failure pattern' library first — 50 specific, documented attribution anomaly types with plain-English AE-ready explanations. No generic platform will ever do this.
riskiest_assumption: That AEs will actually change behavior based on proactive alerts — if they ignore the tool for 3 months, you have a notification bot, not a trust platform.
honest_cost: Real cost: 9 months and $150K minimum before it's credible. Walk away if you don't have historical multi-vendor attribution data already in-house to train on.

## agent_readiness

overall: 54/100 (band D)
memory_required: 21/25
tool_count: 7/25
policy_surface: 11/25
eval_coverage: 15/25

## owasp_codes

- MCP-2: Cross-server prompt injection
- MCP-5: Insecure tool composition
- MCP-7: Unbounded resource consumption

## install_options

bootstrap: https://whycantwehaveanagentforthis.com/api/bootstrap/6bc6c09e-ef3/raw
policy_yaml: https://whycantwehaveanagentforthis.com/api/agent-readiness/6bc6c09e-ef3?format=yaml
badge_svg: https://whycantwehaveanagentforthis.com/api/badge/6bc6c09e-ef3/svg

## next_step

scope_with_human: https://cal.com/sattyamjjain
note: Scope this agent live with a human — 20 min, free, no signup