A groundbreaking approach to solving data privacy challenges in connected mobility through intent-based AI agent coordination, allowing stakeholders to collaborate without exposing sensitive data.
Matt Foster – Sales Director – Multiscale Technologies
Example Scenario: Tire-to-Driver Coordination via Intent-Based Agents
In connected mobility ecosystems, the exchange of sensitive data has long presented a roadblock to effective collaboration among stakeholders. Traditional data-sharing agreements are often complex, time-consuming, and fail due to regulatory constraints and intellectual property concerns. Intent-based coordination through AI-to-AI agent communication offers a revolutionary solution to this challenge.
Consider a practical implementation involving multiple stakeholders: a tire manufacturer (Tier 1 supplier), an automotive OEM managing vehicle systems, a mobility platform operator handling fleet management, and smart city infrastructure. In this ecosystem, each stakeholder deploys specialized AI agents that can communicate without sharing raw data.
Tire AI Agent
Monitors wear patterns, pressure
changes, and traction conditions using localized models confined to the Tier 1 supplier’s domain.
Vehicle Systems Agent
Interprets incoming signals from component agents and adjusts vehicle parameters accordingly while maintaining OEM data sovereignty.
Driver Interface Agent
Translates technical signals into appropriate driver notifications without exposing the underlying data analysis.
Infrastructure Agent
Provides contextual information about road conditions and coordinates responses without accessing vehicle specific data.
When a scenario unfolds4such as a tire experiencing abnormal traction loss4the information flow maintains privacy while achieving the desired outcome. The Tire AI Agent detects the issue and sends an intent signal requesting speed reduction. This signal contains the recommendation without exposing proprietary detection algorithms. The Vehicle Systems Agent verifies the context and modifies driving parameters, while the Driver Interface Agent communicates a simplified alert to the human or AI driver.
This approach delivers multiple advantages: each stakeholder maintains data sovereignty, deployment can occur rapidly through Multiscale’s platform, ecosystem optimization happens without complex contractual arrangements, and the entire system remains explainable and auditable.
Technical Architecture
The technical foundation enabling privacy-preserving AI agents in mobility ecosystems is built on Multiscale’s advanced technology stack. This architecture seamlessly integrates sophisticated AI capabilities with practical deployment considerations across diverse environments and stakeholders.
At the core of this system are Multiscale’s Inverse Solvers and Active Learning Engine, which provide the computational backbone for these intelligent agents. These technologies enable three critical capabilities that make privacy-preserving coordination possible:
Local Learning from Sparse Data
Agents can build robust models even with limited or uncertain information, eliminating the need for massive data sharing. This capability is particularly valuable in scenarios where comprehensive data collection is impractical or restricted.
Predictive Modeling and Simulation
Each agent can generate forward-looking simulations to anticipate outcomes before they occur, enabling proactive rather than reactive responses to changing conditions.
Intent-Based Communication
Rather than exchanging raw data, agents communicate through abstracted outcome intents that convey necessary information without compromising sensitive details or proprietary methods.
The platform architecture includes several key components that make this vision operational. The Agent Builder UI provides domain experts with intuitive tools to define agent logic without requiring advanced programming skills. This democratizes agent creation and allows specialists to embed their expertise directly into the system.
The Agent Communication Protocol establishes a standardized intent-based API layer that facilitates seamless interaction between agents from different stakeholders. This protocol ensures that communications remain focused on outcomes rather than raw data exchange, preserving privacy while enabling coordination.
Finally, the Secure Agent Execution Layer provides flexible deployment options across edge devices, in-vehicle systems, or cloud environments. This flexibility ensures that agents can operate in the most appropriate location for their specific role, balancing factors like latency, processing requirements, and data sensitivity.
Why This Matters Now
The mobility industry stands at a critical inflection point, facing unprecedented pressure to innovate across multiple fronts simultaneously. Electrification demands new approaches to energy management and range optimization. Autonomy requires sophisticated sensing and decision-making capabilities. Safety remains paramount, with consumers and regulators expecting continuous improvements. Amidst these challenges, stakeholders must navigate an increasingly complex regulatory landscape, particularly regarding data privacy and security.
Rising Innovation Pressure
Mobility players face intensifying demands to advance electrification, autonomy, and safety features at an accelerating pace. Traditional development cycles are too slow, and conventional approaches to cross stakeholder collaboration often create bottlenecks rather than acceleration.
AI-to-AI collaboration dramatically compresses innovation timelines by enabling parallel development and seamless integration of specialized capabilities from different sources.
Regulatory Complexity
Global data protection regulations like GDPR, CCPA, and industry-specific frameworks impose strict requirements on how personal and sensitive data can be collected, processed, and shared. These constraints often prevent valuable collaborations that could otherwise deliver significant benefits.
Intent-based communication circumvents many regulatory hurdles by eliminating the need to share raw data, focusing instead on coordination through outcome-oriented signals.
AI-to-AI collaboration offers a rare win-win solution in this complex landscape. By enabling specialized agents to communicate using a common “intent language” while keeping sensitive data securely fenced within organizational boundaries, this approach unlocks ecosystem-level optimization without the traditional barriers.
The applications extend far beyond the tire-to-driver example outlined earlier. Vehicle agents could communicate with traffic management systems to optimize flow and reduce congestion without revealing specific route information. EV charging networks could coordinate with vehicle battery management systems to schedule optimal charging windows without exposing proprietary battery algorithms or personal travel patterns. Fleet management systems could optimize maintenance schedules based on aggregated intent signals from multiple vehicle components without accessing detailed operational data.
The timing is ideal for forward-thinking organizations to adopt this paradigm. Multiscale AI’s platform provides the necessary building blocks to deploy this architecture today, enabling mobility leaders to gain a competitive edge while addressing privacy concerns that have historically hampered industry-wide innovation.
Backed by 25+ Years of World-Class Research
The privacy-preserving AI agent approach isn’t merely a theoretical concept4it’s built on decades of rigorous scientific research and practical expertise. At the helm of this innovation is Dr. Surya Kalidindi, Chief Technology Officer at Multiscale AI and one of the world’s foremost authorities in AI-driven materials and systems optimization.
Dr. Surya Kalidindi’s Distinguished Background
With a Ph.D. from MIT, Dr. Kalidindi has established himself as a pioneering figure in the application of advanced computational methods to complex engineering challenges. His contributions to the field are evidenced by an impressive academic portfolio of over 300 peer-reviewed publications that have garnered more than 30,000 citations from researchers worldwide.
In recognition of his exceptional contributions, Dr. Kalidindi has been named the recipient of the prestigious 2025 AIME Honorary Membership Award, one of the highest honors in the materials engineering community. This accolade underscores his significant impact on advancing the field through innovative research and practical applications.
Dr. Kalidindi’s expertise spans several critical areas that directly inform Multiscale AI’s approach to privacy-preserving agent systems:
Bayesian Methods for High-Dimensional Design
His pioneering work in Bayesian optimization techniques enables agents to make robust decisions even with sparse data4a crucial capability for privacy-preserving systems where complete information sharing is not possible.
Integrated Experiment/Simulation Co-Design
Dr. Kalidindi developed methodologies that seamlessly blend real-world observations with simulated outcomes, allowing systems to continuously refine their understanding while minimizing data collection requirements.
Digital Twins & AI Materials Knowledge Systems
His groundbreaking research into digital representations of physical systems provides the foundation for creating accurate agent models that can operate independently while coordinating through intent based signals.
At Multiscale AI, Dr. Kalidindi leads a team of Ph.D.-level AI/ML specialists who translate these decades of foundational research into practical, high-impact solutions for industrial and mobility leaders. This exceptional depth of expertise ensures that every agent built on the Multiscale platform is scientifically rigorous, engineering-grade, and ready for real-world deployment.
The combination of academic excellence and practical implementation experience makes Multiscale uniquely positioned to deliver on the promise of privacy-preserving AI agents. While many solutions in the market focus primarily on algorithmic approaches without domain-specific understanding, Multiscale’s approach is deeply rooted in both computational expertise and real-world engineering knowledge4creating agents that truly understand the physical contexts in which they operate.
Contact Information
Ready to explore how privacy-preserving AI agents can transform your mobility ecosystem? Connect with our team to discuss your specific use case and discover how Multiscale AI’s technology can address your challenges while maintaining data sovereignty.
Matt Foster
Sales Director, Multiscale AI
Email
matt@multiscale.tech
Phone
+1.650.515.5739
Website
www.multiscale.tech
Our team of experts is ready to provide a personalized consultation to help you understand how our technology can be applied to your specific challenges. Whether you’re an OEM looking to enhance vehicle systems, a Tier 1 supplier seeking to add intelligence to your components, or a mobility platform operator aiming to optimize fleet performance, we have the expertise to support your innovation journey.
Multiscale AI offers various engagement models, from proof-of-concept projects to full-scale implementations. We can work with your technical teams to identify the highest-value applications for privacy-preserving AI agents within your ecosystem and develop a roadmap for deployment that aligns with your strategic objectives.
Contact us today to schedule an initial discovery call and take the first step toward unlocking the power of intent based coordination in your mobility solutions.