Automotive executives and AI developers are reassessing the plans to bring automated driving systems to everyday vehicles. Rising semiconductor and memory costs are impacting the industry and affecting the deployment of Level 3 and Level 4 autonomous driving systems. At AutoSens Europe 2026 , Duong-Van Nguyen, global deputy CEO at VinFast , a Vietnamese EV maker, and Igal Raichelgauz, founder and CEO of Israeli AI company Autobrains , discussed the financial and technical challenges of autonomous driving systems during a session titled “From Monolithic End-to-End to Agentic AI: An OEM View on Affordable, Scalable ADAS.” Moderated by Stav Shvartz from AutoMobility , the conversation focused on moving from complex, end-to-end neural networks to modular AI agents that can help lower production costs and reduce data needs. Rethinking monolithic AI Although some media coverage and industry projections suggest that fully autonomous vehicles are nearing mass-market adoption, real-world deployment of Level 4 driverless vehicles remains limited. “The reality of [autonomous] driving problem is not solved yet,” Nguyen stated during the panel. “It is partially solved in some very well-defined conditions or in areas supported by dedicated infrastructure, high-definition mapping, and continuous connectivity.” To bring more automation to consumer cars, automakers have turned to end-to-end deep learning systems. Companies such as Tesla and BYD use these models to map sensor inputs to driving decisions and vehicle-control commands, including steering, acceleration, and braking. But handling rare situations can require much more computing power, pushing hardware costs beyond what most OEMs can afford. “When you face different situations in the real world, the model gets bigger because you need to have more resources to compute and understand complex corner cases,” Nguyen said. This creates tough choices for carmakers who want to keep prices reasonable and stay within the limits of their onboard electronics, he argued. Shift to agentic architectures To address the computing challenge, Raichelgauz introduced an approach based on agentic AI. He described three generations of driver-assistance technology. The first relied on lots of sensors and rule-based robotics, while the second used large neural networks that cut down on sensors but needed much more computing power. Raichelgauz said the industry is now entering a third generation focused on specialization. Instead of running one huge neural network all the time, agentic AI driving breaks down tasks into smaller, specialized software agents. Each agent handles a specific driving job or situation and runs only when needed. “Instead of solving autonomous driving as one big problem, try to break down the complexity into many similar problems where each is addressed by a specialized driver agent,” Raichelgauz said. From left, Stav Shvartz of AutoMobility, Duong-Van Nguyen of VinFast, and Igal Raichelgauz of Autobrains take part in the “From Monolithic End-to-End to Agentic AI: An OEM View on Affordable, Scalable ADAS” session at AutoSens Europe 2026 in Barcelona. (Source: Pablo Valerio | EE Times) He compared the approach with the way the human brain allocates resources. The human brain uses about 20 watts of power because it only uses the resources needed for each situation, not all at once. Raichelgauz explained that applying this idea to vehicle software lets agentic systems run on much less computing power without losing performance. Because agentic systems train each specialized model separately, automakers also need much less training data—two to three times less, according to Raichelgauz, than they would for large, unified models. This can greatly cut development costs. Financial and testing challenges for automotive OEMs Nguyen said that rising costs for data infrastructure and testing are major challenges for making autonomous driving sustainable. “The cost of data collection and AI develop