AI Support Agents: Security, Scale, or Both?

For any business leader overseeing customer or technical support, the lure of AI agents is obvious: instant responses, tireless operation, and potential cost savings. But as enterprises like Mobileye have discovered, moving from proof-of-concept chatbots to AI agents handling sensitive workloads exposes a classic tension—how do you scale support automation without compromising on the security and governance your enterprise demands?

Off-the-shelf cloud solutions offer speed and simplicity, but they often mean routing confidential queries and data into public infrastructure. For companies in regulated industries, that's a non-starter. The question shifts from 'can we automate support?' to 'can we do it at scale, securely, and without fragmenting our tech stack?'

Mobileye, best known for its work in autonomous vehicles, ran headlong into this problem. Their support teams faced surging demand, and traditional service desk models couldn't keep pace. Instead of choosing between all-cloud or all-on-prem, Mobileye engineered a hybrid approach. They built their AI support agent on Amazon Bedrock AgentCore, but kept a tight rein on sensitive processes by bridging on-premises infrastructure with cloud-based AI capabilities.

The result? A system that routes support queries through secure, internal channels, only engaging cloud AI services where appropriate and safe. This hybrid model lets them tap into the latest advancements in AI without the compliance headaches that come from pushing everything into the cloud. It’s a case study in threading the needle between agility and control.

Once you deploy AI agents at scale, every decision about architecture has ripple effects. Governance isn’t a feature you tack on later—it’s built into the bones of your system. The hybrid model Mobileye adopted means their IT and compliance teams retain visibility into every data flow, access control, and escalation path. This isn’t just about ticking regulatory boxes: it’s about hard-wiring risk management into automation itself.

In the projects we run, we've seen how easy it is for well-intentioned automation experiments to spiral out of control. Without guardrails, AI agents can expose sensitive data, make unauthorized changes, or simply go off-script in ways that are hard to predict. Leaders have to ask: who owns the AI’s decisions, and how do we audit them in real time?

The business case for AI support agents is clear, but the operational case is murkier. Scaling up requires more than just provisioning extra compute: you need systems that log, monitor, and—if necessary—intervene. Mobileye’s hybrid approach creates a template for scaling AI support in environments where oversight is non-negotiable. By using Bedrock AgentCore’s controls and keeping core workflows anchored on-premises, they balance speed with accountability.

For tech leaders, this means the decision isn’t binary. You don’t have to pick between the full flexibility of the cloud and the reassuring walls of your local datacenter. But you do need the engineering muscle to stitch these worlds together, and the discipline to keep governance front and center as support automation scales.

Every leader considering AI support agents now faces a strategic fork. Do you take the fast path to full-cloud AI, accepting the security tradeoffs? Or do you invest in a hybrid model, which demands more upfront work but pays off in compliance and control? There’s no universal answer, but the stakes are real: move too fast and you risk data leaks; move too slow and your support teams drown in manual tickets.

Mobileye’s approach shows it’s possible to square the circle, but only with deliberate design and a willingness to rethink architecture. For enterprises sitting on the sidelines, the window for indecision is closing. The support bottleneck will only get worse—those who act decisively now will shape not just their own operations, but the standards for enterprise AI governance across the industry.