What if deploying production-grade AI agents required zero infrastructure management, came with enterprise observability built-in, and worked easily with your existing on-premises systems?
What happened
What if deploying production-grade AI agents required zero infrastructure management, came with enterprise observability built-in, and worked easily with your existing on-premises systems?
Practical impact for readers
Mobileye , the autonomous driving pioneer with more than 230 million EyeQ system-on-chips deployed across roughly 1,200 vehicle models worldwide, saw an opportunity to free skilled engineers from routine internal ticket status inquiries.
Details worth verifying
Mobileye’s Data Collection Processing pipeline ingests thousands of drive-recording sessions daily, generating a constant stream of status inquiries from engineers and data teams. Each inquiry previously required manual steps across multiple systems – identifying sessions, cross-referencing visualization tools, validating outputs, and reviewing logs – before composing a response. Using Amazon Bedrock AgentCore, Mobileye deployed an AI Support Agent that cut response times by 90% and exceeded 95% accuracy targets, with zero infrastructure overhead. The results were so compelling that Mobileye transformed AgentCore into a self-service platform for teams across the company to deploy their own AI agents.
Who should act or wait
In this post, we’ll explore how Mobileye deployed an AI support agentic solution on Amazon Bedrock AgentCore – from the support bottleneck that sparked the idea, through the proof of concept that validated it, to the hybrid architecture that bridges on-premises systems with AWS cloud services. This approach is relevant for enterprises struggling to scale AI Agents while maintaining enterprise grade governance and security standards.
What is still unclear
As Mobileye’s Data Collection pipeline scaled, 66% of support tickets became routine status inquiries requiring engineers to manually navigate 15 clicks across multiple backend systems. This time-consuming process diverted skilled engineers from complex issues while internal users faced longer wait times.
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