Three ways Agentic AI redefines the in-car experience
- Andy Qiu
- Oct 16
- 4 min read
Updated: Oct 20
Agentic AI is remaking the in-car experience, moving vehicles from passive responders to active agents that can interpret intent and complete tasks on behalf of their users. Traditional voice systems wait for explicit commands, but agentic systems are able to break intent into actions, then coordinate specialist capabilities and follow through, across navigation, digital cabin services and commerce. This shift will change how people will interact with cars, and how manufacturers must design their software hardware and business models.
In this Insight, we unpack each dimension with emerging industry examples and clear recommendations for product and platform teams so OEMs can convert technical capability into repeatable user value and new revenue streams.
Orchestration → turning intent into autonomous multi-agent action.
Learning → enabling continuous adaptation through shared memory.
Monetization → embedding secure, standardized payment capabilities to power new service ecosystems.
1. From a single assistant to an orchestrated system of agents
The change
In place of a single reactive assistant, the cockpit becomes an orchestrated system of specialist agents. A central orchestrator agent translates user intent into tasks and then assigns those tasks to domain expert agents, such as navigation payment, infotainment, and vehicle health. The result is proactive, coordinated action across formerly separate systems.
Examples
NIO Banyan 3 and NOMI agents integrate perception cognition emotion and domain expert agents so a single statement like “I will take my parents out for dinner tonight” triggers automatic coordination of location parking, cuisine, and budget.
Mercedes Benz MBUX Gen 4 uses dialog navigation and knowledge agents that hand off tasks for multi turn context retentive conversation.
IM Motors AIOS ecosystem implements a “No Touch No App” approach where a central agent calls delivery parking and entertainment agents without the user switching apps.
What should OEMs do?
Build an intent-to-action orchestration layer. Replace brittle point to point APIs with a semantic feature bus that lets third party service agents plug in dynamically. Design the cockpit as an extensible service platform rather than a fixed command tree. Finally, prioritise clear agent interfaces service discovery and runtime governance so the system can scale without losing control.
2. From one time tasks to continuous and iterative learning
The change
Cars evolve from stateless task executors into adaptive systems that retain context and improve over time. Agents share a unified memory fabric so collective learning and personalization occurs across agents and over journeys.
Examples
A schedule agent learns a driver’s weekly gym routine, the navigation agent predicts departure time, and the music agent queues the preferred playlist without new commands.
Tasks begun in car such as drafting a note or creating a meeting reminder can be resumed by a home or office agent through cloud shared memory.
NIO, BMW and IM Motors use long short term memory or vector memory layers to retain short term and long term preferences while limiting exposure through on device learning.
What should OEMs do?
Implement a shared context layer that provides a single source of truth for agent state and user preferences. Design for privacy first by keeping raw user data local and sharing only derived insights for global model updates. Finally, include lifecycle controls for memory retention consent and the ability to inspect and erase stored preferences.
3. From product features to ecosystem-driven service models
The change
When agents can both act and pay, they turn vehicles into active nodes in commerce networks. Payment capable agents can discover, book, and settle services on behalf of the user, creating new recurring revenue streams.
Examples
Google AP2 protocol standardises agent initiated payments including programmable settlement options and interoperability across account to account and merchant frameworks.
Mercedes pay plus supports fingerprint-based in car payment for fuel and subscriptions to prove identity and authorisation.
ISO 15118 Plug and Charge enables automatic authentication and billing at EV chargers. Li Auto parking and Meituan ordering agents handle gate access, parking fees, and meal orders through in-car payment and conversational flows.
What should OEMs do? Treat payment-capable agents as monetisation engines. Integrate AP2-ready flows, Plug and Charge, and native biometric authentication into agent workflows. Design an auditable ledger for agent transactions and clear user controls for consent and receipts. Finally, consider revenue share and developer platform economics so third party agents can drive usage while OEMs retain trust and compliance.
Next steps
"Agentic AI changes the cockpit from a reactive interface into an active orchestrator that learns and trades on the user’s behalf. To capture the opportunity OEMs must invest in three core platform layers now. First is an orchestration layer that turns intent into coordinated action. Second is a shared context layer that enables cross agent learning while protecting privacy. Third is a secure payments layer that allows agents to transact in trusted auditable ways. Prioritising these layers will let carmakers move from one off product sales to ongoing mobility service roles that generate new value for users and for the business."
To discuss your agentic AI journey, schedule a complimentary discovery call with one of SBD Automotive's automotive AI experts. And to get started today, download the AI for Automotive Guide from SBD Automotive to inform your roadmap and partner strategy. This detailed report covers OEM AI strategies, short term, mid-term and long-term use cases, including practical case studies, as well as AI partner options to consider.
