It support · Production

Druva's multi-agent copilot for streamlined data protection using Amazon Bedrock

The problem

Enterprises managing complex data environments spend hours manually investigating backup failures across dozens of systems, and tracking the high volume of metrics needed to identify cyber threats leaves organizations vulnerable when any signal is missed.

Workflow diagram · grounded in source

User submits NL query

trigger

“submitting natural language queries related to data protection, backup management, and troubleshooting”

Supervisor agent routes request

routing

“The supervisor agent analyzes the user's input and routes the request to the appropriate sub-agents based on the nature of the query”

Dynamic API selection

ai_action

“we use semantic search to retrieve the top K relevant APIs. This semantic search capability enables the system to adapt to the specific context of each user request, enhancing the Copilot's accuracy, efficiency, and scalability. Once the…”

Data agent retrieves via GET APIs

integration

“The data agent is responsible for retrieving relevant information from Druva's systems by interacting with the GET APIs”

Help agent provides guidance

ai_action

“This agent draws upon an extensive knowledge base, which includes detailed API documentation, user manuals, and frequently asked questions, to deliver context-specific assistance to users”

Action agent executes via POST APIs

integration

“When a user needs to perform critical actions, such as initiating a backup job or modifying data protection policies, the action agent comes into play. This agent interacts with the POST API endpoints to execute the necessary operations”

Human-in-the-loop approval

human_review

“users can provide additional information or explicit approvals by using the user feedback node before the copilot performs critical actions”

Response delivered to user

output

“The user receives a clear and concise response, along with relevant recommendations or guidance, through the user interface”

Reported outcome

The multi-agent copilot enables 90% of routine data protection tasks to be executed through natural language interactions and reduces average time-to-resolution for data security issues by up to 70%, accelerating backup troubleshooting from hours to minutes.

Reported metrics

Routine data protection tasks via natural language90%

Time-to-resolution reduction for data security issuesup to 70%

API selection accuracy (Nova Micro)81%

API selection accuracy (Nova Lite)88%

Show all 12 reported metrics

routine data protection tasks via natural language90%

time-to-resolution reduction for data security issuesup to 70%

API selection accuracy (Nova Micro)81%

API selection accuracy (Nova Lite)88%

API selection accuracy (Nova Pro)93%

API selection accuracy range (Haiku 3, Haiku 3.5, Sonnet 3.5)91% to 92%

average response time (Nova Pro)just over one second

latency (Sonnet 3.5)eight seconds

average output tokens (Sonnet 3.5)291

average output tokens (Nova Pro)86

end-to-end system evaluation score3.3 out of 5

evaluation questions tested in initial development11

Reported stack

Amazon BedrockAmazon Bedrock AgentCore RuntimeAmazon Bedrock AgentCore Gatewaylarge language models

Source

https://aws.amazon.com/blogs/machine-learning/harnessing-the-power-of-generative-ai-druvas-multi-agent-copilot-for-streamlined-data-protection?tag=soumet-20

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The multi-agent copilot enables 90% of routine data protection tasks to be executed through natural language interactions and reduces average time-to-resolution for data security issues by up to 70%, accelerating back…

What tools did this team use?

Amazon Bedrock, Amazon Bedrock AgentCore Runtime, Amazon Bedrock AgentCore Gateway, large language models.

What results were reported?

Routine data protection tasks via natural language: 90%; Time-to-resolution reduction for data security issues: up to 70%; API selection accuracy (Nova Micro): 81%; API selection accuracy (Nova Lite): 88% (source-reported, not independently verified).

How is this it support AI workflow structured?

User submits NL query → Supervisor agent routes request → Dynamic API selection → Data agent retrieves via GET APIs → Help agent provides guidance → Action agent executes via POST APIs → Human-in-the-loop approval → Response delivered to user.