Mahavir Teraiya

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 and 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

Curious to know how Adidas is transforming customer experience and business impact with agentic workflows, powered by Databricks? By leveraging cutting-edge tools like MosaicML’s deployment capabilities, Mosaic AI Gateway, and MLflow, Adidas built a scalable GenAI agentic infrastructure that delivers actionable insights from growing 2 million product reviews annually. With remarkable results: - 60% latency reduction (15.5 seconds to 6 seconds) - 91.67% cost savings (transitioning to more efficient LLMs) - 98.5% token efficiency, reducing input tokens from 200k to just 3k - 20% increase in productivity (faster time to insight) - Empowering over 500 decision-makers across 150+ countries, this infrastructure is set to optimize products and services for Adidas’ 500 million members by 2025 while supporting dozens of upcoming AI-driven solutions. Join us to explore how Adidas turned agentic workflows infra into a strategic advantage using Databricks and learn how you can do the same!