Can AgentCore Stop Cloud Cost Overruns? Real‑Time Monitoring and AI‑Driven Optimization

In the cloud‑native era, rapid digital transformation makes cloud infrastructure essential, but escalating usage leads to uncontrolled cloud costs.

Typical cost‑overrun causes include resource leaks from bugs, forgotten large instances, and mis‑configured auto‑scaling, often discovered only after monthly billing.

Traditional monitoring via Cost Explorer reports or Cost and Usage Report (CUR) requires complex SQL over massive data, is inefficient and not user‑friendly for non‑technical stakeholders.

Solution Overview

The article designs and implements an intelligent cloud‑cost monitoring and alert system. Users interact with the system through natural‑language queries to an AI Agent that provides analysis, optimization suggestions, and anomaly alerts.

Technology Stack

Strands Agents : an open‑source AI Agent SDK from AWS that lets developers build production‑grade agents with minimal code, supporting any model with reasoning and tool use, integrated with Amazon Bedrock, Lambda, ECS.

Amazon Bedrock AgentCore Runtime : a managed serverless environment for deploying agents, handling architecture, security, and scalability challenges.

Core Runtime Features

Serverless hosting : upload container image or Python zip, no server management.

Framework‑agnostic : works with LangGraph, CrewAI, LlamaIndex, etc.

Long‑running support : up to 8 hours per execution.

Unified entry and interaction : Agent Card for discovery and conversation management.

Session isolation via microVM, built‑in identity with IAM, automatic scaling, full‑traceability via CloudWatch.

Architecture: Single‑Agent Dual‑Mode

The agent handles both interactive consulting and automated monitoring, avoiding coordination overhead. In monitoring mode, EventBridge triggers the agent daily; in interactive mode, users ask natural‑language questions.

Key Tools Implemented

Cost anomaly detection using Amazon Cost Anomaly Detection API (Python function detect_cost_anomalies).

Multi‑account cost aggregation via Organizations API and Cost Explorer (function get_multi_account_costs).

Budget monitoring ( get_all_budgets), cost forecasting ( get_cost_forecast), service‑level cost analysis ( get_service_costs), account cost comparison ( compare_account_costs).

Deployment and Runtime Invocation

AgentCore Runtime is created with boto3.client('bedrock-agentcore').create_agent_runtime. A Lambda function ( lambda_handler) invokes the agent via agentcore_client.invoke_agent_runtime, parses the response, and sends alerts through SNS when anomaly keywords are detected.

Testing

Functional tests demonstrate the agent answering cost‑related queries, providing multi‑dimensional analysis, ML‑based forecasts, and actionable recommendations.

Repository Structure

cost_optimization_agent.py          # main agent file
tools/
  cost_explorer_tools.py
  budget_tools.py
  multi_account_tools.py
test_local.py
test_agentcore_runtime.py
deploy.py

Full source code and deployment instructions are available on GitHub.

References

Strands Agents documentation: https://strandsagents.com/1.x/documentation/docs/user-guide/deploy/deploy_to_bedrock_agentcore/

Sample repository: https://github.com/aws-samples/sample_agentic_ai_strands

Project repository: https://github.com/CrazyCha/Agent-cost-optimization