Beyond the Trace: adidas’ Agent Digital Twin for Governance, Cost, and ROI

Your agent answered but can you prove why, what it cost, and what breaks when a tool or prompt changes? AI spend is forecast near $1.5T in 2025 and GenAI at $644B, yet Gartner predicts >40% of agentic AI projects will be canceled by end-2027 as costs rise and controls lag. At adidas (200+ serving endpoints, 300+ data & AI products, 6k+ registered models, 600k+ pipeline runs), we go beyond per-run traces to an Agent Digital Twin: a lakehouse control plane mapping agent→tool→prompt→retrieval→model call→post-processing, then rolling up risk, quality, and unit economics across a fleet. You’ll see a Databricks pattern with MLflow Tracing, Unity Catalog + system tables for audit evidence and DBU/cost signals, plus eval/optimization loops with governance guardrails.

Key highlights:

• Digital-twin control plane for fleets
• Per-hop unit economics + cost leak forensics
• Continuous audit evidence by design
• ROI attribution by agent behavior