it_support · saas · workflow
Druva's multi-agent copilot for streamlined data protection using Amazon Bedrock
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits NL query
The user interacts with the supervisor agent by submitting natural language queries related to data protection, backup management, and troubleshooting.
Tools used
Amazon BedrockAmazon Bedrock AgentCore RuntimeAmazon Bedrock AgentCore Gatewaylarge language models
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.
Results
Time savedup to 70%
Volume90%
Grounding & classification
Source type: technical build writeup
42 fields verified against source quotes, 1 dropped as unverifiable.
agentic workflowai agentconversational aiknowledge searchmulti agent workflowragknowledge basepolicy documentbuilder submittedfailure mode describedhuman review describedmetric backednamed customertools describedvendor confirmedworkflow describedsoftwareautomation rateresolution time reductiontime savedtechnical build writeupcompliance monitoringcustomer supportincident managementit supportagentic task executionescalation workflowrag answering