nOps Accelerates FinOps AI Deployment by 75% Using Amazon Bedrock AgentCore

nOps, an AI-powered cloud optimization platform, has revamped its Financial Operations (FinOps) analytics capabilities by migrating its Clara AI agent to Amazon Bedrock AgentCore. This move replaces their previous self-managed Amazon EKS stack, which utilized LangChain and LangGraph, with a managed, scalable agent runtime. The transition has enabled nOps to accelerate product delivery, improve response accuracy, and streamline operations.

Before the migration, Clara was built on a complex infrastructure involving Kubernetes, Amazon Bedrock model invocation, and orchestration layers, which introduced latency, system complexity, and slowed innovation. The new architecture centers on Amazon Bedrock AgentCore, supported by Databricks Lakehouse for analytics and Lakebase for persistent state, creating a unified, scalable platform.

The redesigned system features a single-agent runtime deployed via Docker on Amazon Bedrock AgentCore, with direct tool access for tasks like data querying and workflow management. This setup reduces latency by avoiding multi-agent routing and enhances response consistency through streaming responses and real-time updates. Customer interactions occur through a Vercel-hosted web app, with requests processed by the agent runtime, which maintains user preferences, organizational facts, and session context.

At the data layer, Clara now leverages Databricks Lakehouse Metric Views for governed, semantic analytics, enabling more accurate and consistent answers compared to raw SQL queries. The system also employs asynchronous workflows using Amazon DynamoDB, SNS, SQS, and API Gateway WebSocket push to handle long-running tasks and provide real-time UI updates.

The impact of these changes is substantial: development velocity has increased by 75%, reducing time-to-market from 10–12 months to just 4 months; response accuracy improved by over 145%, with correctness scores rising to 81.7%; and manual analysis time has been cut by 75%, from two hours to 30 minutes daily. Infrastructure complexity was also reduced by eliminating orchestration layers, allowing engineering teams to focus on product innovation.

Overall, this architectural shift has transformed Clara into a more scalable, accurate, and operationally efficient enterprise AI analytics system, setting a pattern for building agent-native AI solutions in cloud cost management and beyond.