AI-Driven QA Automation Agents

@Microlent

Hello, I have carefully reviewed your project description for developing AI-Driven QA Automation Agents. It seems like you are looking to streamline your QA process by automating test case generation, script building, execution, reporting, and defect logging using autonomous agents. Based on the scope of work outlined, I can see that this project requires expertise in backend development, API integration, Python programming, Playwright, and automation testing. My experience in developing similar automation solutions aligns well with your requirements. To address your needs, I propose to create a modular system that connects to Jira's API, processes acceptance criteria, sitemap, and knowledge base to generate test cases, builds Playwright scripts in Python, executes tests on Windows build agents, and automatically logs defects in Jira with reproducible steps and attachments. I believe my strong problem-solving skills, technical proficiency, and commitment to delivering high-quality solutions make me a suitable candidate for this project. If you are interested in discussing further details or have any questions, feel free to reach out. Let's collaborate to bring your vision to life. You can view my portfolio at https://www.freelancer.com/u/Microlent Looking forward to the opportunity to work together. Sincerely, Rajesh

@adeelpirzada

Hi, I am Adeel. I build LLM-driven automation pipelines, including Boostifai, a multi-tenant SaaS that audits sites and applies fixes automatically through an embedded script rather than a manual process. That same pattern, agents reading structured input and producing working output with a human checkpoint, is what you are describing here. I would start with the Jira connector and test case generator first, since everything downstream depends on the quality of that structured output, then wire the script builder to your existing page-object conventions before touching execution and reporting. I can start this week and work within your 40 hour weekly window. What does your current knowledge base of the application look like, a wiki, a vector store, or something else?

@neerajdhameliya

As an experienced AI and full-stack engineer, I have the perfect skillset to execute your vision of replacing the manual QA flow with autonomous agents. I understand the importance of leveraging AI technology not just for the sake of it, but to truly enhance your team's efficiency and productivity. That's why I'm confident in my ability to connect to Jira's REST API, extract the requisite data you need from acceptance criteria and combine it with the latest sitemap and knowledge base to generate structured test cases using Python. Additionally, one of my recent projects involved creating a Python image-analysis platform, demonstrating not only my proficiency in Python but also in structuring workflows in a clear and modular way. This makes me a great fit for your project as I understand the need for easy extendability. Beyond just executing the automated-test suite, I prioritize thoroughness and accountability in my work. I will not only generate Playwright code but will also store these scripts properly within your existing repository following conventions. Moreover, as part of script execution, I'll diligently capture logs, screenshots, and videos that can help further with analysis if required. Trust me to deliver all this with remarkable on-time and on-budget delivery. Let's revolutionize your software testing process together!

@tangramua

Hello,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Testing / QA, Software Architecture, Software Testing, Test Automation, Continuous Integration, Selenium, Automation, API Development, Script Writing and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@intelliresponse

Hi, I can build this end-to-end autonomous QA workflow using Jira, Python, Playwright, and modular AI agents. I’ll handle test generation, human approval, script creation, Windows execution, reporting, and automatic Jira defect creation with evidence. I’ll keep the architecture clean and extensible for Linux/macOS runners later. Happy to discuss the milestones and timeline.

@offshore9006

Hi there, We're a team of AI automation experts. Proposed modular design: Generator: Jira REST (JQL → stories, AC, defects) + sitemap parser + vector store (Qdrant/pgvector) as persistent app knowledge. An LLM agent outputs schema-validated JSON test cases. Flag ambiguous ACs as "Needs Clarification" for human approval. Script builder: approved cases become pytest-playwright code using your page objects and naming rules (few-shot from repo). Gates: ruff, pytest --collect-only, dry run, then PR. Executor: one webhook/button triggers Windows agents (traces, video, screenshots, JUnit XML). Runner logic is containerizable for Linux/macOS later. Reporter/defects: HTML summary posted as a Jira comment on the story; failures create/update deduplicated defects (test ID + error fingerprint) with repro steps, traces, videos. Orchestration: n8n (no-code) or LangGraph; custom code only for generation/execution. Milestones: M1 Jira→test cases M2 scripts+linting M3 execution+reports M4 defects+hardening Look forward to hearing from you !! Best, Raman

@sufyanasmat38

Hi, we can build this autonomous QA workflow to turn your Jira requirements, sitemap, and application knowledge base into maintainable Playwright tests in Python. We can handle the full pipeline from test-case generation and human approval through script creation, Windows execution, reporting, and automatic Jira defect creation with screenshots, videos, and reproducible steps. We’ll keep the agents modular with clear interfaces so additional runners and integrations can be added later. Our experience with Python, Playwright, REST APIs, Jira integrations, AI agents, automation, and CI/CD fits this workflow well. Could you share your current Jira setup, repository/page-object structure, and knowledge-base format so we can define the milestones and timeline accurately?

@topcreator100

Hi Mate , Good morning! I’ve carefully checked your requirements and really interested in this job. I’m full stack node.js developer working at large-scale apps as a lead developer with U.S. and European teams. I’m offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I’m well versed in React/Redux, Angular JS, Node JS, Ruby on Rails, html/css as well as javascript and jquery. I have rich experienced in Testing / QA, Automation, Selenium, Software Testing, Python, API Development, Test Automation, Continuous Integration, Script Writing and Software Architecture. For more information about me, please refer to my portfolios. I’m ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details.. Looking forward to serve you

@webskillssl

Hi there, I would scaffold your AI QA agents on Playwright's native test-agents flow — planner, generator, healer — wrapped in a light orchestration layer reading specs from a shared knowledge store. Jira access and artifact storage I'd expose through MCP servers, keeping tool calls decoupled from hard-coded wrappers. The real difficulty isn't generating tests but coordinating them: shared state, clean handoffs between generation, execution and reporting, and an AgentBase with pluggable tools so Windows-first logic hides behind a config layer. I build agents, backends and test pipelines daily in Python and Node, and my TYPO3 and Laravel work always carried unit, functional and visual-regression testing — QA is the backbone of how I ship. Closest matches are my n8n workflow suite spanning RAG, MCP and multi-provider LLM chains, and the Freelancer.com automated bidding engine on Python, Vue and MySQL with embeddings and an MCP server. To keep generated code trustworthy, I'd bake your page-object pattern and naming conventions into the prompts as output schemas, then let the healer agent self-repair flaky selectors, attaching screenshots, video and logs to each defect automatically. Happy to sketch a prototype and discuss milestones. Damian Hunziker

@ShaikhAneesa

Hi there, I’ve carefully reviewed the requirements and understand that you need reliable Deriv bots that can analyze market conditions, generate trend-based signals, and execute trades automatically with controlled risk. First, I’ll map the trend-following strategy into precise entry, exit, confirmation, and risk-management rules. Then I’ll build the bot in Python using the Deriv API/WebSocket for real-time market data and order execution, with modular components for analysis, signal generation, and trading. Next, I’ll add configurable parameters, trade logging, error handling, connection recovery, and safeguards to prevent duplicate or unintended trades. Finally, I’ll backtest and forward-test the strategy before deployment and provide the configuration and source code for immediate use. I have experience developing Python-based automation, data-driven analysis, APIs, and algorithmic workflows, and can structure the bot for reliable ongoing operation. If you have an existing trend-following strategy or indicators, please share them so I can implement the logic precisely. Cheers, Aneesa

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@denisSolution

Hi, I'm Denis, a full-stack developer specializing in automation systems that integrate multiple services and orchestrate complex workflows. I understand your multi-agent pipeline: read Jira → generate test cases → create Playwright scripts → execute → log results back to Jira. The key challenge is orchestrating these steps reliably while keeping the system modular. I've built similar systems where agents coordinate across APIs and manage artifacts. The unique aspect here is generating executable code—requiring careful attention to script quality and consistency with your patterns. My approach: I'll review your Jira structure, sitemap format, and knowledge base to understand agent data needs. Then I'll build the workflow orchestrator tying everything together. The test-case generator connects to Jira and flags ambiguous items for review. Once approved, the script builder generates Python code following your page-object pattern. The executor runs tests on Windows agents, captures evidence, and the reporter formats results back to Jira. Code generation quality is critical. I'll use prompt engineering to ensure generated scripts are linted-clean and follow your conventions consistently—building templates, validation checks, and feedback loops where failed runs improve future prompts. One realistic risk: Jira data or acceptance criteria might be ambiguous. I'll have the agent flag items it can't confidently interpret for human review before script generation starts. This pr

@ImranAli01

Hi there, I went through your project description and understand you need an autonomous QA pipeline that will turn Jira requirements, sitemap data, application knowledge, and human-approved test cases into executable Playwright tests and automated defect reports. I’ll build the workflow as modular Python agents covering Jira ingestion, test-case generation, ambiguity detection, Playwright script generation, execution, reporting, and defect creation. Jira REST APIs will provide acceptance criteria, stories, and existing defects, while the persistent knowledge store will provide application context and the sitemap will help agents reason about coverage and navigation. Approved cases will generate Python Playwright scripts following your repository conventions and page-object structure. Execution on Windows build agents will capture logs, screenshots, and videos, with Markdown/HTML reports attached to the relevant Jira story. Failed steps will automatically create or update Jira defects with reproducible steps and supporting evidence. The orchestration layer will use clear approval gates, structured outputs, validation, retries, and modular runner interfaces so Linux and macOS execution can be added later. A detailed project blueprint will be shared within 24–48 hours after the project is awarded, outlining the agent architecture, workflow, testing strategy, and implementation plan. Kind Regards, Imran Ali

@PINESCRIPTERR

Hi, I can build the autonomous QA pipeline as modular agents covering Jira ingestion, knowledge/sitemap context, test-case generation and human approval, Playwright Python script generation, execution, reporting, and automated Jira defect management. I’ll keep the agent workflow deterministic where possible, with validation gates before generated tests are committed or executed, and structure the runner layer so Windows agents can be extended to Linux/macOS later. The implementation can also preserve your existing repository conventions, page-object architecture, artifacts, traceability, and Jira links from requirements through execution results and defects. Question 1: Is your existing Playwright repository already using a defined page-object structure and CI/build system, and should generated scripts be committed automatically or only after the human approval step? Question 2: What knowledge-store format or technology should the agents consume, and do you already have a preferred LLM/provider for test-case and Playwright code generation? Regards, Houssame.

@rajat07me

Your QA flow has a clear bottleneck: generating reliable Playwright coverage from Jira stories is only useful if the chain also handles ambiguity, repo standards, execution evidence, and defect feedback without breaking. I’d structure this as modular agents with a review gate after test-case generation, then separate services for code generation, execution/reporting, and Jira defect sync so it stays extendable to Linux/macOS later. I’ve worked on automation setups where the hard part was not writing tests themselves, but making the pipeline trustworthy end to end—API ingestion, consistent Python output, artifact capture, and clean issue logging back into the team’s workflow. That kind of modularity is exactly what your brief needs if you want this to become a maintainable QA system instead of a demo. - Do you already have a preferred knowledge store/vector layer in place, or should that be part of the implementation? - Is your existing repository already using a fixed page-object pattern we should follow? - Will the one-button trigger start from Jira, CI/CD, or a standalone service endpoint?

@michaelbukachi

Hi, I specialize in Python, autonomous agent architectures, and Playwright test automation. Here is how I can build your autonomous QA agent chain: 1. Jira Story & Spec Parser: - Connect via Jira REST API to extract user stories, acceptance criteria, and linked defect history. - Context Ingestion: Index sitemaps and app documentation into a lightweight vector/context store to ground test scenario discovery. - Output structured test cases in JSON with confidence scoring, flagging ambiguous criteria for human review. 2. Playwright Script Builder: - Generate clean, modular Python Playwright code strictly adhering to the Page Object Model (POM). - Standardized assertions, parameterized selectors, and automated linting (flake8/black) before repo commit. 3. Execution, Reporting & Defect Logging: - Headless execution runner on Windows/Linux build agents capturing console logs, full-page failure screenshots, and video traces. - Generate HTML/Markdown summaries attached directly to Jira stories. - Defect Creator: Automatically file structured Jira bug issues for test failures with stack traces and reproduction steps linked. I can begin by setting up the Jira API connector and initial Playwright generator module immediately.

@Synet

Hi, I will connect your Jira REST API to the autonomous agents so they can read user stories and acceptance criteria alongside your sitemap and knowledge store. I'll write the Python modules to build clean Playwright tests that match your page object pattern and naming rules before saving them to your repository. For execution, I'll set up the Windows build agents to run the suite, capture screenshots, and log every failed step. When a test fails, the agent will open a new defect in Jira with the logs and link the evidence straight to the related story. I will also make sure the agent flags any unclear acceptance criteria for human review before any code is generated. How do you want to handle authentication tokens securely for the Jira API connection during test execution? Best, Salma Noreen

@Tanmoy236

Hi There! I specialize in building Python automation workflows, with 9+ years of experience, 465+ completed projects, and Top 1% verified status. Your Jira → AI test-case generation → Playwright/Python scripting → Windows execution → reporting → Jira defect flow can be built as a modular agent pipeline. I’d structure each agent with clear inputs/outputs, human review gates for ambiguous criteria, repository/page-object integration, artifact capture, and Jira traceability so the entire workflow runs through one API call or button while remaining extensible to Linux/macOS. Would you prefer the agents to use a specific LLM/provider, or should the architecture remain model-agnostic?

@AneesaIBA

Hi there, I have read and understood the project details. You want to replace a manual QA workflow with a modular agent pipeline that reads Jira requirements, combines them with your sitemap and persistent application knowledge, generates reviewable test cases, creates Playwright Python scripts, executes them on Windows runners, reports evidence, and automatically manages Jira defects. I’ll design the workflow as separate agents with clear handoffs and validation gates: Jira ingestion and requirement analysis, test-case generation with ambiguity detection, Playwright/Python script generation following your existing repository and page-object conventions, execution/reporting, and Jira defect management. I’ll use Jira REST APIs, Playwright, Python, structured outputs, and persistent state/knowledge storage, with artifact handling for logs, screenshots, videos, and reproducible defect evidence. The orchestration will remain modular so additional runner environments can be introduced later. Could you share your current Jira project structure, sitemap/knowledge-store format, repository/page-object conventions, and Windows build environment? I can build the workflow around a single API/button trigger with validation at each stage, automated linting and Playwright execution, Markdown/HTML reporting, and Jira-linked defects with evidence. Cheers, Rabia Shaikh