Quality assurance teams across modern software development face a new reality. AI enabled applications do not behave like traditional systems. Outputs shift based on context. Responses vary across runs. Prompts and data changes alter behavior without code updates. Traditional QA workflows struggle under these conditions.

This blog explains why our QA team built agentic AI test workflows, how those workflows function in practice, and what measurable change followed. The content also explains how this capability supports customers building AI driven products across industries.

The Problem Our QA Team Faced

The QA team worked on multiple AI enabled products including conversational systems, recommendation engines, and predictive workflows. Testing challenges increased with every release cycle.

Test cases required constant revision. Automation scripts broke due to small prompt or logic updates. Senior QA engineers spent more time rethinking coverage than executing tests. Release discussions focused on delays rather than readiness. The core issue was not execution speed. The core issue was scaling QA thinking across changing AI behavior.

Traditional QA assumes determinism. AI driven software testing introduces probability, variation, and drift. Static test cases lose relevance fast. Manual reasoning does not scale. Automation alone does not solve coverage.

This gap forced a rethink of QA workflows from first principles.

Why Agentic AI Became Necessary

AI systems introduce several testing realities.

• Behavior changes based on context and input phrasing
• Outputs vary across runs even with identical inputs
• Model updates and prompt changes alter behavior without code changes

QA teams face repeated test redesign, growing cognitive load, and late discovery of gaps. More automation does not fix the underlying issue.

The missing element was adaptive reasoning inside the QA workflow itself. The question that changed direction was simple.

What if QA workflows included intelligent agents that analyzed changes, generated scenarios, structured test cases, and executed tests alongside human testers. The goal was not replacement. The goal was leverage.

Agentic AI as a QA Co Worker

The approach treated agentic AI as a co worker rather than a replacement.

QA engineers remained responsible for quality decisions. Agents handled repetitive reasoning and execution tasks. Human judgment stayed central.

Each agent followed three rules.

• A single defined role
• Clear boundaries
• Structured inputs and outputs

This structure preserved trust, auditability, and accountability. Agents never made release decisions. Agents supported faster and deeper QA thinking.

Types of Agents Built for Test Workflows

Over time, the QA team built multiple agents across the testing lifecycle.

• Test scenario generation
• Detailed test case creation
• Automation script generation and execution
• Persistent storage of test artifacts and results

The real breakthrough came from connecting these agents into a coordinated workflow rather than isolated tools.

A Real World QA Bottleneck

The team worked on an AI enabled application with frequent requirement changes.

Challenges included rapidly shifting AI behavior, high effort across design and automation, outdated scripts, and late discovery of coverage gaps. Releases carried risk despite strong QA practices.

The issue was workflow scalability rather than tooling.

The Agentic AI Test Workflow

The team introduced three collaborating QA agents plus a human oversight role. Each agent handled a specific responsibility.

Agent One. Test Scenario Analyst

Responsibilities included reviewing requirements and changes, identifying impacted areas, and generating comprehensive scenarios including edge cases.

Results included faster scenario creation, early risk visibility, and reduced dependence on manual brainstorming.

Agent Two. Test Case Structuring Agent

Responsibilities included converting scenarios into step by step test cases, defining preconditions and expected results, maintaining QA standards, and storing test cases for reuse.

Results included standardized test cases, faster reviews, and reduced manual design effort.

Agent Three. Automation and Execution Agent

Responsibilities included converting test steps into standalone Playwright scripts, saving reusable automation suites, executing tests on demand, and capturing results in text based files.

Scripts remained independent and reusable without regeneration.

Results included faster automation readiness, reduced scripting effort, and consistent execution feedback.

Agent Four. Human QA Agent

Responsibilities included monitoring the workflow, reviewing outputs, making judgment calls, and validating final outcomes.

Results included maintained accountability, trust in outcomes, and preserved QA ownership.

How Agents Collaborated

The workflow followed a controlled sequence.

• Scenario analyst determined coverage
• Test case agent defined execution detail
• Automation agent executed and reported

Supporting technologies included Autogen for agent orchestration, system prompts defining agent roles, round robin collaboration for context handoff, MCP servers for secure tool access, file system MCP for artifact storage, and Python orchestration for auditability.

QA engineers reviewed outputs at every stage. AI supported the workflow. QA owned outcomes.

Measured Outcomes

The workflow delivered measurable improvements.

• Thirty five to forty percent reduction in test design effort
• Around thirty percent faster automation readiness
• Earlier detection of coverage gaps
• Reduced late stage rework

Management gained predictability during release sign offs. QA updates shifted toward risk and readiness discussions. Testers gained confidence in understanding AI behavior rather than reacting to failures.

AI Maturity Within the QA Team

Building agentic workflows changed how the QA team approached AI.

The team learned to understand behavior drift, treat prompts and data as testable artifacts, and challenge AI outputs through structured validation. QA engineers evolved from tool users into AI aware quality professionals.

From a leadership perspective, agentic AI reflects how software development changes rather than a passing trend. Organizations such as Microsoft highlight multi agent systems with clear orchestration and human oversight as a core pattern for complex AI work.

Customer Impact

Customers benefit from faster releases, stronger coverage, reduced QA cost through reuse, and higher confidence before production deployment.

Testing services extend beyond execution. Customers receive AI ready QA teams equipped for modern systems.

How ISHIR Helps

ISHIR builds and operates agentic AI test workflows for customers building AI enabled software products. Teams work with organizations to design QA workflows that adapt to changing AI behavior while preserving accountability and auditability.

ISHIR supports clients across Dallas Fort Worth, Austin, Houston, and San Antonio in Texas. Delivery teams operate across India, LATAM, and Eastern Europe to provide global scale with local leadership.

Customers gain structured agentic QA workflows, AI aware testers, and predictable release confidence.