A Practical Guide for QA Engineers: Building 18 AI Agents with 5 Essential Tools

Introduction: AI Agents Are Reshaping the Software Testing Industry

While most QA engineers are still manually writing test cases and pulling all-nighters running regression tests, one 35-year-old test engineer has completely transformed his workflow using AI Agents. By leveraging five powerful tools — Coze, LangChain, Skills, Dify, and Cursor — he built 18 production-ready Agents that handle nearly all of his repetitive daily tasks.

An AI Agent is an artificial intelligence system capable of autonomously perceiving its environment, making decisions, and executing actions. Unlike traditional AI chatbots, Agents are goal-oriented, capable of autonomous planning, and able to invoke external tools. They can break down complex tasks into multiple sub-steps and sequentially call different tools or APIs to complete them. In software testing, the value of Agents lies in their ability to understand testing context (such as requirement documents and code changes), autonomously plan testing strategies, and invoke the appropriate tools to generate test cases, execute scripts, or produce reports — forming an end-to-end automation loop.

This isn't a proof of concept — it's a real productivity revolution. Toss in a requirements document, and test cases are generated automatically. After a performance run, an analysis report is exported with one click. Without writing a single line of code, you can accomplish what used to require an all-night session.

Let's break down the design philosophy and use cases for all 18 AI Agents.

Skills: A Testing-Specific Tool Platform with 3 High-Frequency AI Agents

Skills is a dedicated tool platform designed specifically for testers. The author built three core Agents on it:

Auto-Convert PRs to Test Cases

When a developer submits a Pull Request, the Agent automatically parses the code changes, identifies the impact scope, and generates corresponding test cases. This eliminates the time testers spend reading through code diffs line by line.

A Pull Request (PR) is a core mechanism in code collaboration. After developers submit code changes to a version control system (such as GitHub or GitLab), they use a PR to request merging those changes into the main branch. A PR contains diff information (the specific additions, deletions, and modifications), commit messages, and linked issues. When an AI Agent parses a PR, it uses AST (Abstract Syntax Tree) analysis or semantic understanding to identify the affected modules, functions, and data flows, thereby determining the impact scope. This automated analysis capability replaces the tedious process of testers manually reading code diffs — especially valuable in large projects where a single PR might involve changes across dozens of files, making manual analysis highly prone to oversights.

Auto-Generate Requirements Checklists

After a requirements document is fed in, the Agent automatically extracts key test points and generates a structured checklist. This is especially useful during requirements review, enabling quick identification of overlooked test scenarios.

Intelligent Regression Test Case Selection

Based on the impact scope of the current changes, the Agent intelligently selects the subset of existing test cases that need regression testing, avoiding the resource waste of full regression runs.

Traditional regression testing faces a core dilemma: full regression takes too long, while random selection risks missing critical scenarios. The intelligent selection Agent's technical approach typically includes: static analysis based on code changes (identifying upstream and downstream dependencies of modified modules), risk assessment based on historical defect data (prioritizing modules with frequent bugs), and mapping relationships between test cases and code (establishing correlations through requirements traceability matrices or code coverage data). The AI Agent adds semantic understanding on top of this, comprehending the business implications of changes to make more precise selection decisions — compressing regression test cases to 20%-30% of the full suite while maintaining a defect detection rate above 95%.

These three Agents cover the highest-frequency daily needs of QA engineers. According to the author's estimates, they save at least two hours per day, and efficiency can double when combined with other tools.

Coze: No-Code AI Agent Building for 5 Practical Scenarios

Coze is a no-code AI application building platform launched by ByteDance. With an extremely low barrier to entry, it's ideal for testers without a programming background.

Coze was officially launched in late 2023 with the core philosophy of enabling non-technical users to build AI Agents through visual drag-and-drop interfaces. The platform integrates multiple large language models (such as the Doubao model) under the hood and provides a plugin marketplace, knowledge base, and workflow orchestration modules. Users only need to define the Agent's role prompt, attach a knowledge base, and configure workflow nodes to create a fully functional AI application. The essence of no-code is encapsulating complex model calls, context management, and tool orchestration into visual components, allowing testers to build powerful automation tools without understanding the underlying API call logic.

The author built five Agents on the platform:

• Requirements to Test Cases: After inputting a requirements document, it automatically outputs structured test cases
• Auto-Generate Test Documentation: Automatically produces testing reference materials based on requirements documents
• Performance Analysis Report Generation: After a performance test run, generates a visual analysis report with one click
• Requirements Q&A Bot: Team members can ask the Agent about requirement details anytime, eliminating the need to repeatedly track people down for confirmation
• New Hire Onboarding Assistant: Newly hired testers can quickly learn about project context and testing standards through the Agent

The Requirements Q&A Bot delivers particularly outstanding value — when developers ask "what does this requirement actually mean?", you just point them to the bot. Testers no longer need to explain things repeatedly, and newcomers can get up to speed instantly.

LangChain: Deep Customization for 4 Complex Scenario Agents

LangChain is currently the most popular framework for building LLM-powered applications, suited for those with a technical background who need deep customization.

LangChain was created by Harrison Chase in 2022 as an open-source framework designed specifically for building applications based on large language models. Its core components include: Chain (chaining multiple LLM calls together), Agent (letting the LLM autonomously decide which tools to invoke), Memory (conversation memory management), and Retrieval (Retrieval-Augmented Generation, or RAG). LangChain's Agent module supports the ReAct (Reasoning + Acting) paradigm, where the model first reasons about what to do next, then executes the corresponding action, looping until the task is complete. This architecture is particularly well-suited for multi-step, dynamic-decision-making test scenarios — for example, an API test might need to first log in to obtain a token, then call a business API, and finally verify the database state. This kind of multi-step orchestration is exactly where LangChain Agents excel.

The author built four Agents:

• Multi-Step API Testing Agent: Handles complex API call chains, automatically orchestrating the call sequence and parameter passing across multiple endpoints
• Natural Language to SQL Query: Describe your query needs in natural language, and the Agent automatically generates the corresponding SQL statement for test data verification
• Auto-Generate Automation Test Scripts: Based on test scenario descriptions, automatically generates executable automation test scripts
• Complex Business Flow Orchestration Agent: For scenarios with complex business processes, automatically orchestrates test steps

LangChain's advantage lies in its extreme flexibility. If you want to break free from the "tool operator" role and pursue deep customization, LangChain is the way to go. However, the learning curve is steeper than Coze — you'll need Python programming skills and a basic understanding of LLM API calls.

Dify: Open-Source with Private Deployment for 3 Enterprise-Grade Agents

Dify is an AI application platform that supports both open-source and private deployment, making it especially suitable for enterprises with data security requirements. The author built three Agents:

• Private Knowledge Base Agent: Builds a private knowledge base from internal test documentation, standards, and historical defects, with the Agent answering questions based on this data
• Intelligent Test Case Classification Agent: Automatically classifies and tags test cases for organized management
• API Debugging Agent: Supports rapid API debugging and verification

The Private Knowledge Base Agent is powered by RAG (Retrieval-Augmented Generation), one of the core technologies in enterprise AI applications today. Here's how it works: internal documents are first converted into high-dimensional vectors using an Embedding model and stored in a vector database (such as Weaviate or Pinecone). When a user asks a question, the system first retrieves the most relevant document fragments through semantic similarity search, then passes these fragments as context to the large language model to generate an answer. This ensures the AI's responses are grounded in actual enterprise documents rather than the model's general knowledge, effectively avoiding the "hallucination" problem. Meanwhile, all data flows within the internal network, meeting security and compliance requirements for industries like finance and healthcare.

Dify's core advantage is support for enterprise-grade private deployment — data never leaves the internal network, and non-technical staff can easily build Agents through a visual interface without writing code.

Cursor: AI Coding Powerhouse with 3 Efficiency-Boosting Agents

Cursor is one of the hottest AI coding tools available today, developed by Anysphere and built as a deep modification of VS Code. It integrates top-tier LLMs like GPT-4 and Claude, with core capabilities including: code completion (auto-continuation with the Tab key), Cmd+K inline editing (select code and describe your modification intent in natural language), Chat mode (converse with your entire codebase), and Composer mode (batch code generation across files). What makes Cursor unique is its ability to understand the entire project's code context — not just the current file. This means when generating test scripts, it can accurately reference existing utility functions, configurations, and data models in the project, producing code that runs immediately rather than requiring extensive manual adjustments.

The author built three Agents with it:

• Auto-Generate Test Scripts: Describe a test scenario, and Cursor generates a complete automation script directly
• Vulnerability Fix Agent: Automatically identifies potential issues in code and provides fix recommendations
• Script Optimization Agent: Refactors and optimizes existing test scripts

The author reports that writing a test script used to take half an hour, but now it takes just three minutes with Cursor — a 10x efficiency improvement.

Why QA Engineers Must Embrace AI Agents

Protect Your Career and Boost Your Competitiveness

Manual testing is becoming increasingly replaceable. Without upgrading your AI skills, getting phased out is only a matter of time. Mastering AI Agent building means you transition from an "executor" to a "designer." According to multiple industry surveys, over 40% of companies had already begun introducing AI tools into their testing processes by 2024, and this proportion is expected to exceed 70% by 2026. The core value of QA engineers is shifting from "how many test cases you can execute" to "how efficient a testing system you can design."

Say Goodbye to Pointless Overtime and Focus on High-Value Work

Hand off repetitive work to AI so you can focus your energy on more valuable exploratory testing and quality strategy. Exploratory Testing is a method that relies on the tester's experience and intuition to simultaneously design and execute tests. It requires human creativity and business insight — high-value work that AI cannot easily replace in the near term. Leaving work on time is no longer a luxury.

Zero Experience? You Can Still Get Started Quickly

The author himself started from manual testing with minimal programming background, figuring things out step by step. No-code platforms like Coze and Dify have dramatically lowered the barrier to entry.

Conclusion: Tool Selection Guide and Learning Path for All 5 Tools

These 18 AI Agents cover the complete testing workflow: from requirements analysis, test case generation, and script writing to regression execution and report output. Each of the five tools has its own strengths:

Tool	Key Features	Best For
Skills	Testing-specific, ready out of the box	All testers
Coze	No-code, quick to learn	Those without programming skills
LangChain	Flexible customization, powerful features	Those with technical backgrounds
Dify	Open-source, private deployment	Enterprise-level needs
Cursor	AI-powered coding, extremely efficient	Scenarios requiring code

In 2025, the testing world is no longer about the speed of manual execution — it's about who can get AI to do the work for them. We recommend QA engineers start with Coze or Dify, building 1-2 simple Agents to experience the results firsthand, then gradually dive deeper into LangChain and Cursor to build your own AI-powered automated testing workflow. Here's a suggested learning path: Phase 1 — use Coze to build a requirements-to-test-cases Agent (1-2 days to get started); Phase 2 — use Dify to build a team knowledge base (about 1 week); Phase 3 — learn Python basics and use LangChain for complex scenarios (2-4 weeks); Phase 4 — use Cursor to boost daily coding efficiency (continuous improvement through regular use). Take it step by step — each phase delivers tangible value.

Key Takeaways

• Build 18 testing Agents using five tools — Skills, Coze, LangChain, Dify, and Cursor — covering the complete pipeline from requirements analysis to report generation
• No-code platforms like Coze and Dify dramatically lower the barrier to building AI Agents, enabling testers without programming skills to get started quickly
• AI Agents can automate repetitive tasks like test case generation, regression selection, and script writing, with efficiency gains of up to 10x per task
• LangChain is ideal for deep customization of complex scenarios, while Dify supports enterprise-grade private deployment — different tools for different needs
• In 2025, the core competitive advantage for QA engineers is shifting from manual execution to AI workflow design capabilities