A real-world-inspired architecture talk about embedding an AI agent into the operational workflow of a highload data pipeline. We walk through a cascade failure scenario: corrupted data enters the pipeline, Kafka queues get stuck, storage pressure grows, thousands of Kubernetes pods start failing and rescheduling, etcd degrades, and PostgreSQL becomes a secondary pressure point. Then we show how an agent built with AWS Bedrock AgentCore, LangChain, and MCP/Gateway could detect early signals, isolate corrupted messages, suggest human-approved fixes, protect cluster stability, and turn noisy telemetry into actionable recovery steps.

How to ensure data streaming from databases to cloud solutions such as GBQ using Symfony 6, RabbitMQ and Kafka?