Volumez Automates Cloud Data Infrastructure Decisions with Agentic AI on AWS

Customer

The Challenge

Volumez wanted to reduce the manual effort required to tune cloud data infrastructure while keeping infrastructure decisions fast, safe, and observable.

• - Operators spent time monitoring workload conditions and adjusting infrastructure when performance, capacity, or cost patterns changed.
• - Reactive monitoring made it harder to anticipate capacity and performance issues before they affected workload efficiency.
• - Any AI-driven infrastructure action needed strong policy boundaries, observability, retries, and safe-failure behavior.

Why Tactical Edge AI

Volumez needed an implementation partner that could combine predictive ML, generative reasoning, and infrastructure automation without introducing a new operations burden.

Tactical Edge AI used managed AWS services and a mixed compute model to fit each workload, then added policy-controlled orchestration, tracing, security, retries, and safe-failure behavior. The design integrated with the existing Volumez control plane instead of creating a separate AI system.

The Solution

Amazon Kinesis Data Streams and AWS Glue ingest and prepare infrastructure telemetry for analysis. Amazon SageMaker uses historical telemetry for workload and capacity prediction, while Amazon Bedrock provides operational reasoning, summarization, anomaly interpretation, and recommendations.

Amazon OpenSearch Service and Amazon S3 provide contextual retrieval and durable data storage. AWS Lambda provides the event-driven control loop that evaluates telemetry and model outputs, applies defined policies, and invokes approved scaling or resource-adjustment APIs.

Amazon ECS on AWS Fargate handles heavier orchestration and control-plane integrations. DynamoDB stores workflow and policy state, while CloudWatch and X-Ray provide metrics, logs, traces, alarms, and end-to-end latency visibility. If a model or downstream API call fails, the workflow can take no scaling action rather than execute an uncertain change.

Results

• - The production solution maintained consistent sub-100 millisecond AI-driven orchestration latency.
• - Manual tuning and scaling interventions fell from 12 per month to 4, reducing manual intervention by 67 percent and beating the target of at least 50 percent reduction.
• - The implementation gave Volumez a governed path from telemetry to prediction, reasoning, decision, and infrastructure action.

Benefits

Next Steps

Volumez can continue refining predictive models, automation thresholds, and policy boundaries as workload patterns evolve, while expanding automated optimization and cost-to-performance analytics.