How Mobileye transformed support operations using Amazon Bedrock AgentCore

來源原標題

How Mobileye transformed support operations using Amazon Bedrock AgentCore

作者

Adi Jabkowski

原始標籤

Amazon Bedrock · Amazon Bedrock AgentCore · AWS Lambda · AWS Secrets Manager · Customer Solutions

原始連結

https://aws.amazon.com/blogs/machine-learning/how-mobileye-transformed-support-operations-using-amazon-bedrock-agentcore/

摘要原文

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? 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. 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. 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. 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. Traditional automation approaches – scripting, static workflows, and rule-based decision trees – proved inadequate. While these could handle predictable queries, they lacked the contextual understanding needed to interpret the variability of real-world support requests. The team chose to build an AI agent capable of understanding context and adapting to diverse inquiry patterns. Before full production rollout, Mobileye’s team conducted a proof of concept targeting 95% accuracy in ticket classification with sub-2-minute response times. The agent uses Anthropic Claude foundation models, accessed through Mobileye’s internal LLM Gateway that provides governed, quota-managed access to foundation models on Amazon Bedrock . The critical enabler was the Model Context Protocol (MCP), which gave the agent real-time access to the drive-data processing platform’s APIs -allowing it to query session status, retrieve processing logs, and pull diagnostic information during inference. This meant the agent went far beyond simple ticket classification, becoming an intelligent investigator capable of handling inquiries that were previously resolved through time-consuming manual effort. When users inquired about drive-recording sessions, the agent could determine current state and respond with answers: confirming completed sessions with access details, surfacing specific errors with debugging recommendations and log links for failures, or guiding users through submission processes for missing requests – all in under two minutes with no human intervention. After successfully proving the concept with their AI agent, Mobileye needed to take it to production. This transition required a solution that could meet enterprise-grade requirements while maintaining the flexibility they had demonstrated in their proof of concept. After careful evaluation, they chose to deploy their AI Agent on Amazon Bedrock AgentCore, a fully managed platform to build, connect and optimize AI agents at scale with any framework or model. The decision came down to five key factors: This last point was particularly important. Mobileye’s internal ticketing system operated on-premises and was inaccessible from AWS, so any solution would need to bridge that divide without compromising security or compliance. Following the successful proof of concept, Mobileye moved to production with a sophisticated hybrid architecture featuring clearly defined component roles across two environments. The following table describes the on-premises components that handle local ticketing operations and bridge to cloud-based AI processing. The following table describes the AWS cloud components that provide serverless compute, security, and observability infrastructure for the AI agent. The following table describes Mobileye’s own services that provide data connectivity and governed model access for the agent. The automated workflow follows a streamlined four-step process: The production deployment of Mobileye’s AI Support Agent on AgentCore delivered results that consistently met or surpassed initial targets. Support engineers are freed to focus on complex work – and the team didn’t have to build the infrastructure themselves, because AgentCore provides it out of the box. “Amazon Bedrock AgentCore enabled us to build a production-grade AI agent that delivers measurable business value while maintaining the security and operational standards our enterprise requires. The AI Support Agent has transformed our support operations, saving our engineers hundreds of hours previously spent on routine ticket lookups, and freed them to focus on what they do best.” — Shay Margalit, Principal Engineer, Mobileye With the AI Support Agent delivering results in production, Mobileye faced a new challenge: most developers across the organization lacked the AWS credentials or infrastructure access to deploy agents on AgentCore independently. To address this, the Mobileye Cloud Infra team built an internal agent deployment platform. This internal managed service enables Mobileye developers to deploy production-grade AI agents without AWS expertise or cloud credentials. The process follows these steps: The provisioned infrastructure aligns with Mobileye’s standards for security, cost governance, and operational excellence. CloudWatch Alarms on code exceptions help verify issues surface immediately. What began as a single proof-of-concept has evolved into an enterprise-wide platform where teams can deploy secure, monitored, cost-tracked agents in minutes rather than weeks. The path from manual processes to intelligent automation is shorter than you think, and the results, as Mobileye has shown, can be extraordinary.