# Designing a custom AI agent for repetitive QA workflows

DevFeed: [Designing a custom AI agent for repetitive QA workflows](<https://devfeed.tech/articles/designing-a-custom-ai-agent-for-repetitive-qa-workflows-22590.md>)

Original publisher: [Read original article](<https://medium.com/amex-gbt-technology/designing-a-custom-ai-agent-for-repetitive-qa-workflows-0eee8dd0f267?source=rss----60a0578f4096---4>)

Author: Rimple Sharma

Published: 2026-05-13T09:34:47Z

Content type: tutorial

Language: en

Sources: [Amex GBT Technology](<https://devfeed.tech/sources/amex-gbt-technology.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Automation](<https://devfeed.tech/topics/automation.md>), [test](<https://devfeed.tech/topics/test.md>), [test data](<https://devfeed.tech/topics/test-data.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [pull-requests](<https://devfeed.tech/topics/pull-requests.md>), [consistency](<https://devfeed.tech/topics/consistency.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-skills](<https://devfeed.tech/tags/ai-skills.md>), [automation](<https://devfeed.tech/tags/automation.md>), [configuration](<https://devfeed.tech/tags/configuration.md>), [consistency](<https://devfeed.tech/tags/consistency.md>), [pull-requests](<https://devfeed.tech/tags/pull-requests.md>), [qa](<https://devfeed.tech/tags/qa.md>), [software-testing](<https://devfeed.tech/tags/software-testing.md>), [test-automation](<https://devfeed.tech/tags/test-automation.md>), [test-data](<https://devfeed.tech/tags/test-data.md>)

## AI overview

This article defines a repetitive QA workflow involving configuration updates, test-data additions, mapping checks, coverage validation, and pull requests. It argues that the main bottleneck is contextual decision-making and cross-file validation, which motivates designing a custom AI agent.

## Source excerpt

Part 1: Core problem definition, three pillars of an effective agent & why structured instructions matter.Source: AI generated image Engineers on QA teams spend a disproportionate share of their time on work that follows a consistent pattern: updating configurations, modifying test artifacts, executing focused validations, and managing pull requests. The tasks are well-defined but the repetition adds up fast. Individually, none of this is complicated. But taken together, these tasks are: Repetitive Error-prone Hard to repeat reliably During my time in automation, I kept running into the same problem. Every release brought the same requirements. New datasets had to be added so the test suite could cover the scenarios tied to them. It was never a one-time effort. The same work had to be repeated across different combinations, every single time. The process typically looked like this: 1. Updating multiple configuration files. 2. Adding the corresponding test data. 3. Making sure everything was mapped correctly. 4. Validating coverage and consistency. 5. Committing the changes and raising a PR. Each step on its own was straightforward. But strung together and repeated across releases, it consumed a meaningful chunk of time that could have gone elsewhere. Problem breakdownFig 2.0 Pain of manual repetitive process (Source: AI generated image) Each step is straightforward. But together, context switching between files, making manual edits, double checking mappings, it added up to 30 to 60 minutes per release cycle in our case. Miss one mapping, introduce a typo, and the build breaks. It wasn't hard work, just repetitive. And that's exactly the kind of work that makes test suites brittle over time. Even with automation in place, every iteration still required someone to identify what had changed, figure out which files were affected, and make sure everything stayed consistent across the board. The framework we had was solid. Scripts handled the heavy lifting well. But there