How Engineers Build a Production-Ready Multi-Agent Banking AI on Google Cloud

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A major retail bank handling 400,000 monthly support calls set out to reduce telephony costs by replacing routine customer interactions with an AI-powered banking assistant. Engineers began with a simple LLM-backed chatbot but quickly encountered tool overload when connecting it to 20–35 internal banking APIs, causing unreliable tool selection and high latency. To address this, the system was redesigned as a multi-agent architecture using Google Cloud's Vertex AI Agent Engine, with specialized sub-agents handling accounts, transactions, and service requests under a central orchestration layer. The Model Context Protocol (MCP) was introduced to decouple the agent's reasoning logic from direct API integrations, improving modularity and maintainability. The final production design also incorporates zero-trust authentication, PII masking, and full observability across Google Cloud Platform to meet enterprise security and compliance standards.

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