CloudOps Cost Optimizer Agent

Autonomous AI agent for AWS—monitor costs, optimize resources, and save cloud spend automatically with intelligent, continuous cloud management.

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Inspiration

Cloud costs are a silent pain for every tech company. Teams rarely have enough time or expertise to constantly monitor and optimize their AWS resources. As cloud usage grows, so does the risk of accidental waste and budget overruns. Inspired by the vision of truly autonomous cloud management, we set out to build an AI agent that could do the heavy lifting—an intelligent system that enables savings, efficiency, and peace of mind, automatically.

What it does

CloudOps Cost Optimizer Agent is an autonomous AI solution for AWS that continuously monitors cloud usage, analyzes expenses, and proactively recommends or applies resource optimizations. It leverages advanced LLM reasoning (Bedrock/SageMaker) to interpret data, decide actionable steps such as rightsizing or shutdowns, and then notifies your team on Slack or email. No more manual checks—just plug in and secure your savings.

How we built it

Framework: Python for all core logic and automation

AWS Integration: Used boto3 SDK to access Cost Explorer and CloudWatch, plus Bedrock AgentCore for orchestration and reasoning

*Architecture: * Modularized into resource monitoring, cost analysis, optimizer, and notification classes

Configurable: YAML files allow flexible thresholds, notification settings, and schedules

*Workflow: * The agent runs on a schedule or on-demand, fetches AWS data, feeds it to the AI reasoning engine, decides actions, runs optimizations (via Lambda/API Gateway or SDK), and notifies users

Demo: Bash scripts and stubs allow easy demonstration and testing

Challenges we ran into

  • Ensuring the project works across multiple AWS regions and scales well for large cloud accounts

  • Getting meaningful optimization recommendations out of raw cloud data

  • Balancing autonomy and safety—automated actions that are auditable and reversible

  • Integrating reliable notifications and error handling so users never miss critical updates

  • Navigating AWS iam permissions and account setup for secure automation

Accomplishments that we're proud of

  • Designed a flexible, extensible agent architecture using the latest AWS Bedrock/AgentCore paradigms

  • Delivered a proof-of-concept agent that can run end-to-end: monitor, decide, optimize, notify

  • Automated real cost savings in demo accounts during testing

  • Packaged the system for rapid deployment and easy customization by other teams

What we learned

  • How to wrangle cloud data and costs with AWS APIs and Bedrock LLMs

  • Importance of modular design for AI-powered automation

  • Best practices for secure and scalable cloud ops code

  • That truly autonomous optimization unlocks major budget efficiencies with minimal oversight

What's next for CloudOps Cost Optimizer Agent

  • Deepen integration with multi-cloud (Azure, GCP) for cross-platform savings

  • Enable more sophisticated optimization strategies powered by agentic AI reasoning

  • Add Slackbot and dashboard features for interactive recommendations and controls

  • Open source the project so every DevOps engineer can automate cloud savings!

Built With

  • agentcore
  • bedrock
  • cloudwatch
  • cost-explorer
  • costoptimizer
  • ebs
  • iam
  • slack
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