For the past decade, the industry has relied on Machine Learning (ML) for predictive analytics—using historical data to forecast outcomes. While valuable, ML is passive; it provides insights but requires human intervention to act.

We are now witnessing the emergence of a transformative new capability: Agentic AI. Unlike its predecessors, Agentic AI is not merely a tool for analysis; it is an autonomous actor capable of perception, reasoning, decision-making, and execution.

This white paper provides an exhaustive analysis of Agentic AI's role in the EV battery sector. It moves beyond the hype to explore the technical architecture—including Model Context Protocols (MCP), Vector Databases, and RAG systems—that enables these agents to function in an industrial setting.

Agentic AI

@ Automotive Battery

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We accompany you on your journey into the era of Agentic AI

The Agentic AI white paper was created by oetti-ds GmbH, an independent consulting boutique with a focus on machine learning, artificial intelligence, and agentic AI.

Its clients include well-known companies such as Mercedes-Benz, Deutsche Bank, EnBW, REWE digital, GfK, arvato, TÜV Rheinland, and Schwäbisch Hall.

Michael Oettinger is the founder and managing director of oetti-ds GmbH and a recognized expert in artificial intelligence with more than 20 years of project experience in the fields of artificial intelligence, machine learning, and data analytics.

He is the author of the professional book Data Science & AI (3rd edition, 2023) and teaches artificial intelligence as a lecturer at Stuttgart Media University.

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