How Mobileye transformed support operations using Amazon Bedrock AgentCore
Mobileye deployed an AI Support Agent on Amazon Bedrock AgentCore to automate routine internal ticket status inquiries, cutting response times by 90% and exceeding 95% accuracy targets The solution leverages the Model Context Protocol (MCP) to give the agent real-time access to on-premises drive-data processing platform APIs, enabling it to act as an intelligent investigator rather than a simple classifier A hybrid architecture bridges Mobileye's on-premises ticketing system with AWS cloud compo
Analysis
TL;DR
- Mobileye deployed an AI Support Agent on Amazon Bedrock AgentCore to automate routine internal ticket status inquiries, cutting response times by 90% and exceeding 95% accuracy targets
- The solution leverages the Model Context Protocol (MCP) to give the agent real-time access to on-premises drive-data processing platform APIs, enabling it to act as an intelligent investigator rather than a simple classifier
- A hybrid architecture bridges Mobileye's on-premises ticketing system with AWS cloud components, using a Local Orchestrator to handle ticket extraction and response posting while AgentCore's serverless runtime manages agent execution
- Mobileye's internal LLM Gateway provides governed, quota-managed access to Anthropic Claude foundation models on Amazon Bedrock, ensuring enterprise-grade security and compliance
- The success of the proof of concept led Mobileye to transform AgentCore into a self-service platform, enabling other teams across the company to deploy their own AI agents
Why It Matters
This case study demonstrates a practical blueprint for enterprises seeking to scale AI agents while maintaining strict governance, security, and hybrid infrastructure requirements—common pain points for organizations with legacy on-premises systems. The integration of MCP for real-time API access during inference represents a significant advancement over static, rule-based automation, showing how agentic AI can handle complex, context-dependent workflows that previously required human intervention.
Technical Details
- Model & Governance: The agent uses Anthropic Claude foundation models accessed through Mobileye's internal LLM Gateway, which provides governed, quota-managed access to models on Amazon Bedrock, ensuring enterprise compliance and controlled usage.
- Model Context Protocol (MCP): MCP serves as the critical enabler, granting the agent real-time access to the drive-data processing platform's APIs during inference. This allows the agent to query session status, retrieve processing logs, and pull diagnostic information dynamically, transforming it from a ticket classifier into an intelligent investigator.
- Hybrid Architecture: On-premises components include a Local Orchestrator (handles ticket extraction and response posting) and the internal ticketing system. AWS cloud components include AgentCore Runtime (serverless agent execution with automatic scaling), eliminating infrastructure management overhead.
- Performance Metrics: The proof of concept targeted 95% accuracy in ticket classification with sub-2-minute response times; production deployment exceeded the 95% accuracy target and achieved a 90% reduction in response times.
- AgentCore Platform: A fully managed platform for building, connecting, and optimizing AI agents at scale, supporting multiple agentic frameworks, comprehensive built-in observability, and serverless infrastructure that auto-scales to handle support volume spikes.
Industry Insight
- Enterprises with on-premises legacy systems can adopt a hybrid agent architecture that keeps sensitive data and ticketing infrastructure local while leveraging cloud-based AI compute, reducing the security and compliance risks associated with full cloud migration.
- The Model Context Protocol (MCP) is emerging as a key standard for enabling AI agents to interact with real-time external systems during inference, moving beyond static training data to dynamic, context-aware decision-making—a capability essential for production-grade support automation.
- Mobileye's pivot from a single-use agent to a self-service platform illustrates a scalable governance model: once enterprise-grade agent infrastructure is proven, it can be productized internally, accelerating AI adoption across teams while maintaining centralized control over model access, observability, and security policies.
Disclaimer: The above content is generated by AI and is for reference only.