# DataAgents: How we turned 9 months of analysis into 10 days

DevFeed: [DataAgents: How we turned 9 months of analysis into 10 days](<https://devfeed.tech/articles/dataagents-how-we-turned-9-months-of-analysis-into-10-days-22572.md>)

Original publisher: [Read original article](<https://medium.com/capital-one-tech/dataagents-how-we-turned-9-months-of-analysis-into-10-days-8d6ed482f5d7?source=rss----3db3a67cb648---4>)

Author: Capital One Tech

Published: 2026-06-09T22:42:18Z

Content type: tutorial

Language: en

Sources: [Capital One Tech](<https://devfeed.tech/sources/capital-one-tech.md>)

Topics: [Cloud](<https://devfeed.tech/topics/cloud.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Azure](<https://devfeed.tech/topics/azure.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [azure](<https://devfeed.tech/tags/azure.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-platform](<https://devfeed.tech/tags/cloud-platform.md>), [data-analysis](<https://devfeed.tech/tags/data-analysis.md>), [false-positives](<https://devfeed.tech/tags/false-positives.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [least-privilege](<https://devfeed.tech/tags/least-privilege.md>), [security](<https://devfeed.tech/tags/security.md>)

## AI overview

This engineering deep dive describes the DataAgents pattern for analyzing heterogeneous cloud resources at scale. It focuses on cloud resource dormancy detection across AWS, Azure, and Google Cloud Platform, using entity-specific criteria, confidence-based prioritization, and documented reasoning. The article reports reducing the analysis effort from an estimated 6-9 months to 10 days.

## Source excerpt

An engineering deep dive into the pattern that changed how we approach large-scale classification problems. Every engineering team has that project sitting in the backlog. The one where someone says, "We really should analyze all of these," and the room goes quiet. Everyone knows what "all of these" means -- hundreds of entities, complex rules, no clear starting point. For us, it was cloud resource dormancy detection. We had around 350 distinct cloud resource types spread across AWS, Azure and Google Cloud Platform (GCP). Each type has different behavior patterns. An EC2 instance sitting idle looks nothing like a dormant Amazon S3 (S3) bucket or an unattached Elastic IP. Detecting dormancy required understanding what "active" means for each specific resource, then writing detection logic that wouldn't flood operations teams with false positives. Traditional estimate: 6-9 months of expert analysis. Actual time: 10 days. Here's how we did it, and more importantly, here's the reusable pattern behind it. The problem with large-scale analysis: Before we get to the solution, it's worth naming the pattern that makes these projects so painful. It shows up everywhere: Cloud resources - Which of our 350 resource types are dormant? Data governance - Which of our 800 tables have quality issues we should monitor? Security - Which of our access entitlements violate least-privilege principles? Compliance - Which of our 500 policy controls need remediation? In every case, the structure is similar -- a large catalog of heterogeneous entities, entity-specific rules that don't generalize, unknown priorities and a high cost for getting it wrong. The traditional approach is not just slow. It's structurally limited. You get coverage of the "obvious" cases, inconsistent logic across analysts, and tribal knowledge that evaporates when people leave. What you need is something that can assess each entity, apply consistent criteria, prioritize by confidence and document its reasoning. The DataA