Automotive research landscape currently is driven by emerging technologies such
as software-defined vehicles, advanced infotainment systems, and increasingly
automated driving functions. This situation calls for a bigger need for
efficient, comprehensive, and agile research methods. Traditional methods
require significant manual effort, leading to information synthesis and
dissemination bottlenecks. After doing a thorough research on how research is
carried on in automotive companies, it is inferred that a lot of time is spent
on gathering information and integrating it with proprietary knowledge rather
than on analysis or synthesis of the information. There are tools and platforms
with artificial intelligence (AI) advancement that help with deep research of a
particular topic, and there are also tools and platforms that help with
synthesis of proprietary information within automotive organizations. But there
is a lack of a framework that dynamically integrates the aspect of deep research
with the proprietary information within the organization and draws out action
items and action plans for the research to be effective and efficient. The
agentic AI framework introduces efficient multi-agent orchestration and seamless
integration of proprietary automotive data with external research sources,
incorporating principles of building effective multi-agent systems, key metrics,
validation techniques, impact and also the future potential. Initial validation
demonstrates a 50% reduction in research time, a 50% faster time to insight, and
much more impact.