{
  "id": 6299293,
  "title": "How HPE Zerto built an agentic troubleshooting system with Amazon Bedrock",
  "url": "https://urgent.news/2026/09/08/how-hpe-zerto-built-an-agentic-troubleshooting-system-with-amazon",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-08T16:15:23.000Z",
  "source": {
    "name": "AWS Machine Learning",
    "slug": "aws-machine-learning",
    "url": "https://aws.amazon.com/blogs/machine-learning/how-hpe-zerto-built-an-agentic-troubleshooting-system-with-amazon-bedrock/"
  },
  "original_language": "en",
  "account": "HPE Zerto, a company specializing in hybrid and multi-cloud infrastructure resilience, has developed an agentic troubleshooting system utilizing Amazon Bedrock's AI capabilities. This system aims to assist with configuration issues, system health monitoring, and rapid issue resolution. Deployed within Zerto's on-premises environment, the system offers a natural language interface for users to engage with AI-powered assistance.\n\nThe architecture comprises a UI layer for user interaction, an agentic layer housing AI agents, and an intelligence layer handling data processing. Amazon Bedrock, chosen for its secure enterprise deployment and model flexibility, powers the system. Content security is ensured using Amazon Bedrock Guardrails, while per-tenant quotas and limits are managed through AWS CloudWatch, DynamoDB, and Lambda. Observability is provided via CloudWatch telemetry data.\n\nThe agentic system streamlines data protection and disaster recovery, offering sub-agents to handle various tasks such as investigating health issues, performing setup tasks, and accelerating feature adoption. This integration with Zerto's existing UI enables efficient troubleshooting without adding operational burden, particularly beneficial during critical events like ransomware attacks or service disruptions.",
  "summary": "HPE Zerto built an agentic troubleshooting system powered by Amazon Bedrock that runs on-premises inside the customer environment. This post describes the multi-agent architecture, the on-premises deployment model built with Strands Agents, and the engineering challenges of grounding agents in live disaster recovery data.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "AWS Machine Learning",
        "title": "How DiDi built intelligent contact center QA with Amazon Bedrock",
        "url": "https://urgent.news/2026/09/08/how-didi-built-intelligent-contact-center-qa-with-amazon-bedrock",
        "published": "2026-09-08T16:11:11.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}