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- Building AI Agents for Dev Teams: A Practical Guide - Quash
AI agents are transforming how engineering and QA teams work—from smart test execution to code-aware workflows This guide breaks down when to use an agent (vs a tool), how to implement one with real-world integrations, and how Quash uses them to power automated, scalable testing
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From healing broken test flows to predicting high-risk areas, AI agents are helping QA teams move faster while maintaining quality and reducing manual effort Let’s look at the key capabilities that make AI testing agents practical and efficient
- Building AI Agents to Automate Software Test Case Creation
To streamline this process, the DriveOS team at NVIDIA developed Hephaestus (HEPH), an internal generative AI framework for automatic test generation HEPH automates design and implementation for various tests, including integration and unit tests
- AI Agents in QA Testing: Is 2025 The Year Everything Changes?
AI agents allow software teams of all sizes to expand their QA test coverage, with no extra time cost, even for complex processes like end to end testing In fact, with an AI agent you'll test far more in the fraction of the time it takes for your external QA processes to come full cycle - which leads us to… 2
- Understanding the “Agent” in Agentic AI: A Game-Changer for QA Teams
Modern QA teams face unstable test scripts, delayed releases, and brittle automation that can't keep up with rapidly evolving interfaces Agentic AI promises to change that—not by improving scripts, but by replacing them with intelligent agents
- Automating QA Processes with AI Agents - A Modern Approach to Test Case . . .
By offloading routine test case creation to AI agents, QA teams can elevate their focus to higher-value activities that truly require human judgment and creativity
- A Complete Guide on AI Agents For Software Testing
In short, AI agents for software testing act like intelligent assistants to testers, performing repetitive and mundane tasks, allowing testers to focus on decision-making and larger QA goals How Does an AI Testing Agent Actually Work? AI testing agents work like smart assistants for your QA team
- Automated Testing AI and ML: Practical Guide for QA Teams
AI and ML enhance automated testing by making it dynamic rather than rule-based For example, instead of hardcoding test inputs, an ML model can generate inputs based on how users actually interact with the application This helps uncover test scenarios that traditional scripts might miss Another common technique is self-healing tests
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