• The Need for Intelligent Test Automation
• What is Tx-SmarTest? 
• Key Features of Tx-SmarTest 
• Business Benefits of Using Tx-SmarTest 
• Future-Proofing Your QA with AI and ML
• Summary 

The automation testing market is projected to reach $49.9 billion by 2026. This represents a fundamental shift in how enterprises are approaching software quality assurance. Businesses are investing heavily in AI and ML technologies to upscale intelligent test automation solutions where precision, quality, and agility are necessary. However, to support this, organizations will need a robust tool like Tx-SmarTest to facilitate faster releases enabled with DevOps CI/CD processes. Earlier, the testing process used to be slow and full of bugs because most QA tasks were handled manually. But today, automation and AI are leading the charge. 

This blog will discuss how Tx-SmarTest facilitates this shift and offers businesses a smarter test automation solution.

The Need for Intelligent Test Automation

As software systems become complex, traditional test automation struggles to keep pace with changing business requirements. Manual scripting, rigid test cases, and maintenance-heavy frameworks can slow release cycles and increase the risk of bugs in production. This is where intelligent test automation offers a smarter, more adaptive approach.

Intelligent automation integrates machine learning, predictive analytics, and self-healing capabilities to help teams test faster and more efficiently. It reduces manual effort and improves test coverage and accuracy. In today’s agile and DevOps-driven environments, it’s becoming a necessity.

Tx-SmarTest exemplifies this intelligent approach. It combines AI-driven components like the Accelerator for rapid code generation, the Migrator for seamless transitions, the Analyzer for in-depth insights, and the Predictor for forecasting potential defects. Together, these components enhance test coverage, reduce defect slippage, and streamline the automation process, making Tx-SmarTest a pivotal solution in modern test automation strategies.

What is Tx-SmarTest?

Tx-SmarTest is an AI-enabled, comprehensive platform developed by Tx to accelerate software testing and enhance quality. It seamlessly integrates with DevOps CI/CD pipelines, leveraging artificial intelligence (AI), machine learning (ML), deep learning, and neural network algorithms to identify edge test cases and automate them efficiently.

The platform comprises four core components:

• Accelerator: Expedites automation through code generation, documenting test cases, creating automation skeletons, and increasing test coverage to reduce defect slippage.
• Migrator: Facilitates seamless migration of key assets and automation scripts, ensuring continuity and efficiency in testing processes.
• Analyzer: Employs intelligent bug tracking to analyze and identify system errors, enhancing the accuracy of testing outcomes.
• Predictor: Utilizes predictive analytics to forecast potential defects, enabling proactive decision-making and risk mitigation.

Key Features of Tx-SmarTest

Tx-SmarTest stands out because it can integrate with various tools across the software development lifecycle. It connects with popular DevOps CI/CD systems, test management platforms, and defect tracking tools, ensuring that testing is not a siloed activity but a continuous part of development.

Another standout feature is its support for Tx-HyperAutomate frameworks, which enhances script reusability and allows for rapid automation scaling.

Additionally, Tx-SmarTest provides intuitive and role-based dashboards that deliver actionable insights across different levels of the organization, from QA teams to executive leadership. These dashboards simplify decision-making by offering real-time data on test results, defect trends, and automation progress. Together, these features make Tx-SmarTest a powerful solution for enterprises aiming to modernize their testing practices while maintaining speed and quality.

Business Benefits of Using Tx-SmarTest

Tx-SmarTest offers a wide range of business benefits that can significantly improve the efficiency and effectiveness of software testing. Accelerating automation through AI-driven code generation reduces the need for manual intervention, allowing teams to deploy test cases much faster. This boosts the overall speed of the testing process and accelerates time-to-market. Additionally, it helps discover potential impacts by analyzing data from the codebase and test assets. This ensures that testing efforts are focused on the most critical areas of the application, improving coverage and minimizing the chances of defects slipping through.

The accelerator also enhances error detection with intelligent bug tracking, enabling teams to identify system errors early in development. This reduces the cost and effort associated with fixing defects later on. Tx-SmarTest’s seamless migration capability ensures that organizations can smoothly transition their key test assets and automation scripts from older systems to more modern ones without disruption, saving time and resources.

Future-Proofing Your QA with AI and ML 
 

 Summary

Tx-SmarTest is an AI-powered test automation accelerator by Tx that uses UiPath Task Mining components to modernize quality assurance through predictive analytics, automated script generation, and seamless CI/CD integration. It addresses common challenges in traditional QA by reducing manual effort, increasing test coverage, and enabling faster releases. With core components like Accelerator, Migrator, Analyzer, and Predictor, Tx-SmarTest helps organizations achieve agile, efficient, and intelligent testing. Contact our experts now to know more about how Txs’ accelerators can help you upscale your QA initiatives.

The post Tx-SmarTest – Unlock Smarter QA with AI-Powered Platform first appeared on TestingXperts.

1. An Overview of Autonomous AI
2. How does AI Support Autonomous Data Engineering?
3. Enhancing Data Analytics with Autonomous AI
4. Benefits for Data Teams
5. How Can Tx Assist You in Improving Data Quality?
6. Summary

Data engineering has always been a trending topic in the business world. And why not? It plays a key role in enabling data-driven decision-making and fueling innovation by facilitating the usage of next-gen technologies like AI, ML, robotics, etc. As enterprises deal with heavy volumes of data, growing real-time insights requirements, and complex technology integrations, outdated data workflows will surely hit their limits. That’s where autonomous data engineering, or autonomous AI-enabled data engineering, enters. Its main objective is to automate the entire data engineering lifecycle using AI and ML methodologies. It will reshape how data gets discovered, collected, imported, transformed, and activated.

An Overview of Autonomous AI

Autonomous AI refers to AI systems that can act with minimal human supervision and perform various tasks, such as automating repetitive tasks and analyzing data sets. These systems operate independently, learn from data, adapt to changes, and set objectives. Think of autonomous AI as a robot in a warehouse that can navigate between obstacles, spot and bring materials, and adjust to the changes in its path. Let’s look at the core components that make autonomous AI systems work:

• Perception: AI senses and interprets the surroundings via sensors, computer vision, and NLP.
• Planning: AI leverages the collected data to make informed decisions and plan actions using technologies such as ML, deep learning, decision trees, and reasoning and logic.
• Action: AI takes action in the real world through software agents, autonomous systems, robotics, and actuators.
• Databases: AI systems leverage databases to store and retrieve information and learn from it to gain experience and improve performance.

How does AI Support Autonomous Data Engineering?

Next-gen autonomous AI agents can assist by independently planning, executing, and monitoring data-related tasks. They can divide complex problems into small units, link with tools, and optimize data pipelines. Let’s take a detailed look at how autonomous AI supports data engineering tasks:

• Automated Data Profiling and Quality: AI algorithms analyze new data sources to understand data types and schemas and identify quality issues (if any exist). This automates data profiling and ensures data quality throughout the lifecycle. AI also detects odd patterns in data to indicate missing points or inaccuracies.
• Smart Data Integration: AI algorithms can map and transform data from disparate sources with different formats and structures. This facilitates smart data integration to reduce the complexity of unifying data from multiple systems. It also automates string manipulation, unit transformation, and other data cleaning tasks.
• Data Pipelines Streamlined: AI-powered automation can reduce pipeline downtime and improve efficiency by automating code reviews, code generation, and warehouse optimizations. Tools like AWS Glue and Apache Airflow leverage ML to automate pipeline orchestration, enabling self-healing capabilities and dynamic resource allocation.
• Predictive Maintenance: AI-enabled predictive maintenance of data infrastructure can help identify potential system failures and inefficiencies. This will help reduce downtime and optimize performance.
• Data Augmentation: AI can augment existing data or generate synthetic data, giving data engineers additional resources. This will be very helpful when dealing with sensitive or limited data.

Enhancing Data Analytics with AI

AI enhances data analytics capabilities by automating repetitive tasks, identifying hidden data patterns, and improving accuracy. It also accelerates data processing to support faster insights and better decision-making. Let’s take a look at how it impacts data analytics:

• Automating tasks like data cleaning, preprocessing, and transformation frees analysts up for strategic work.
• ML algorithms can detect anomaly patterns in data to deliver precise insights for data engineering teams.
• AI can eliminate human errors and inconsistencies, thus improving data analytics capabilities.
• AI algorithms can perform time series analysis to forecast future demands or identify seasonal demands in sales data.
• NLP enables AI systems to process and extract relevant insights from unstructured data, expanding the scope of data analytics for better business insights.

Benefits for Data Teams

AI benefits data teams by automating repetitive and time-consuming tasks such as data cleaning, transformation, pipeline monitoring, and documentation. This enables data engineers and analysts to shift their focus from maintenance work to more strategic initiatives like building advanced analytics systems or improving data architecture. AI-driven tools can detect anomalies, optimize data flows, and provide pipeline self-healing capabilities. It leads to greater system reliability and reduced downtime. By accelerating the preparation and delivery of clean, high-quality data, teams can produce insights faster and more confidently.

Moreover, autonomous AI can enhance collaboration across roles by generating real-time documentation, maintaining data lineage, and supporting self-service analytics for non-technical users through natural language interfaces. It can enforce governance by automatically identifying sensitive data and monitoring compliance with regulations. It will speed up model development for data scientists by recommending features, tuning models, and evaluating performance. Autonomous AI can help data teams scale efficiently, maintain better data quality, and make smarter, faster decisions.

How Can Tx Assist You in Improving Data Quality?

In the current AI-driven digital era, enterprises depend heavily on data for business decision-making. However, to ensure the effectiveness of data, they require robust testing services. At Tx, we offer AI-driven, customized testing solutions to validate data quality and integrity. Partnering with Tx can help you achieve greater agility, competitiveness, and efficiency in the data-driven landscape. We incorporate AI-driven test automation and intelligent data profiling to detect outliers, predict data quality issues, and prioritize test cases based on risk impact. 

Our proactive testing approach helps you identify issues early in the data pipeline before they affect analytics, reporting, ML models, or decision-making. Our expertise in AI solutions enhances test coverage through smart test data generation and adaptive learning from past defects. By combining domain expertise with AI-enhanced automation, we can assist you in maintaining high data quality even in complex, high-volume, or rapidly changing environments.

Leveraging autonomous AI in data engineering can change how businesses manage data by automating complex workflows and streamlining processes. Improving data integration and pipeline efficiency to enable real-time analytics ensures higher quality and faster insights. Tx supports this shift with robust testing services that detect and resolve data issues early. Our AI-driven test automation and profiling tools enhance data quality, reduce downtime, and support compliance, empowering your data teams to focus on strategy and innovation.

The post Engineering Smarter Data Pipelines with Autonomous AI first appeared on TestingXperts.

In the current technology scenario, where almost everything is AI-integrated, quality control is becoming one of the main forces behind the success of software products. Tracking software development and testing status drastically improves quality levels. It helps monitor developers’ and testers’ efforts while ensuring that every Dev and QA parameter is followed. But what would be the most efficient tool for tracking the status? Tx-Insights is one of the in-house accelerators that keeps track of software development and testing status in real-time across the CI/CD pipeline. It helps ensure software quality, meets user requirements, and facilitates informed decision-making throughout the SDLC.

What is Tx-Insights?

Transparency is a key requirement for an enterprise’s seamless, continuous delivery pipeline. Fast feedback is another important aspect of transparency for large-scale product development, which is managed by multiple agile teams. Tx-Insights, an in-house analytical DevOps dashboard by Tx, enables enterprises to gain an end-to-end visual view of their CI/CD pipelines. This tool integrates with various systems within an enterprise, extracts relevant data, saves it, and makes the recorded metrics available via a dashboard view. The metrics include:

• Test coverage percentage

• Number of security violations

• Risk tracking

• DevOps maturity, and more.

The Tx-Insights collector integrates a configuration management database to support this metric’s data collection, including application details like health status and stakeholders’ details.

Why do Enterprises Struggle with CI/CD Visibility?

Enterprises often struggle with CI/CD visibility as it requires advanced technical administration expertise. Let’s take a look at the challenges associated with CI/CD in the modern enterprise ecosystem:

• Lack of logging and monitoring leads to missed malicious activities that delay detecting and mitigating security vulnerabilities. Without sufficient visibility, detecting the root cause of issues during CI/CD becomes difficult, making debugging and fixing time-consuming.

• Siloed visibility and insights limit the understanding of CI/CD processes for development, operations, and security teams. This results in inefficient problem-solving as teams do not have the full context of the problem and its impact on the pipeline.

• Unauthorized modifications increase security risks as attackers might gain access to the CI/CD pipeline and introduce buggy code or vulnerabilities. Poorly handled access controls create issues that hackers can exploit to collect sensitive data or modify the pipeline.

• Insufficient testing results in faulty code release that might disrupt services and impact the overall user experience.

Tx-Insights provides a single, end-to-end dashboard view of the enterprise CI/CD pipeline, establishing a clear logging and monitoring mechanism. It allows teams to collaborate and communicate better, reducing bottlenecks and establishing a continuous learning and improvement culture.

Key Features and Functionalities of Tx-Insights

Centralized DevOps Metrics View:

Tx-Insights single dashboard monitors key DevOps performance indicators across teams and tools. It covers deployment frequency, build success rates, lead time, and more. It eliminates the need to pull data manually from various systems, saving time and improving visibility.

CI/CD Pipeline Monitoring and Analysis:

The platform tracks every stage of an enterprise’s CI/CD pipeline, offering clear insights into pipeline duration, failure points, and bottlenecks. With this data, teams can pinpoint inefficiencies and optimize their automation workflows.

DevOps Maturity Measurement:

Tx-Insights assesses DevOps setup against defined benchmarks, measuring pipeline speed, test coverage, failure recovery times, and other measurable factors to highlight where Dev and QA processes stand and where they need work.

Risk Identification:

By linking development metrics (like code changes or test failures) with operational indicators (such as incident frequency or downtime), Tx-Insights helps teams detect patterns that may cause deployment risks. This allows for early intervention and better release stability.

Agile and Continuous Improvement:

Tx-Insights breaks down metrics over time, giving a clear view of trends. It supports sprint-based development by allowing users to track improvements or regressions from sprint to sprint, helping guide planning and retrospectives.

Business Benefits of Leveraging Tx-Insights

• Consolidates view of CI/CD and DevOps data into a single, accessible dashboard. Real-time visibility allows teams to make faster and better-informed decisions at every development and deployment stage.

• Automatically tracking pipeline metrics such as test pass rates, build times, and release frequency enables teams to maintain consistent quality.

• By quantifying progress across sprints and releases, teams can identify what’s improving and what’s not and where immediate attention is needed.

• The platform offers structured metrics to measure the maturity of DevOps practices in terms of automation, speed, and stability.

• Tx-Insights updates metrics in real time, helping Agile teams stay aligned with sprint goals and make small but meaningful changes without losing momentum.

• When performance slips below these standards, Tx-Insights flags the deviation, enabling immediate action before it impacts product stability or release timelines.

Summary

Consolidating critical DevOps metrics into a unified dashboard empowers teams to make informed decisions, improve product quality, and manage risk effectively. Designed to support large-scale Agile environments, Tx-Insights helps eliminate silos, track DevOps maturity, and drive continuous improvement. Partnering with Tx will ensure your business can access intelligent, scalable, and performance-focused solutions to strengthen your software delivery processes.

The post Turn Siloed Metrics into Business-Driven Insights with Tx-Insights first appeared on TestingXperts.

Protecting sensitive data against cyber threats is one crucial step in the software application industry. The rising number of cybercrimes and security threats has dramatically changed how businesses operate and innovate. Traditional DevOps practices can no longer keep up with high-velocity, continuous delivery software development. One of the reasons is that a separate cybersecurity and QA team handles the security aspect in the DevOps ecosystem at the end of the SDLC. DevSecOps seeks to cover this gap by integrating security controls throughout the SDLC. Tx-DevSecOps is an accelerator that assists QA teams in utilizing its high-speed, shift-left approach for continuous security across development and testing.

The Need for Integrated Security in DevOps

The process of integrating security into DevOps is called DevSecOps. Due to the increasing sophistication and frequency of security threats, it has become crucial for modern development processes. Enterprises embed security practices throughout their DevOps pipeline to identify and address vulnerabilities as soon as they appear to reduce risk and improve security posture. This enables organizations to deliver software quickly and on a scale without degrading their IT security posture. Here’s why integrating security with DevOps is essential:

• DevSecOps facilitates early identification of vulnerabilities and provides quicker and cost-effective remediation measures.

• With proactive vulnerability mitigation, enterprises can reduce the risk of data breaches and reputational damage.

• By integrating security in the development pipeline early, organizations reduce the cost of fixing issues post-deployment.

• Automated security tools and practices enable faster software releases without degrading security.

Role of Tx-DevSecOps

Tx-DevSecOps is an advanced security automation accelerator and an extension of Tx-DevOps, a DevOps framework by Txs’ DevOps Center of Excellence (DCoE). Although Tx-DevOps helps teams improve the time-to-market ratio by streamlining development and operations through DevOps Consulting, IaC, CI/CD implementation, and test automation, it still requires transformative processes, organizational culture, and technology shifts.

Tx-DevSecOps addresses the growing demand for secure development practices by seamlessly integrating security into every stage of the SDLC. By leveraging the shift-left approach and latest automation tools, Tx-DevSecOps supports early vulnerability detection, robust application security, and continuous compliance.

Key Features of Tx-DevSecOps

Tool-Agnostic Integration:

Tx-DevSecOps is designed to integrate seamlessly with any continuous integration (CI) pipeline. It enables enterprises to embed security into their development workflows without disrupting deployment processes. Its flexible, tool-agnostic approach ensures compatibility across diverse technology stacks.

Automated Security Checks:

Security is automated throughout the DevOps lifecycle using Software Composition Analysis (SCA), Static Application Security Testing (SAST), and Dynamic Application Security Testing (DAST). These automated validations help identify vulnerabilities in code, dependencies, and runtime behavior, ensuring secure application development from start to finish.

Compliance Management:

Tx-DevSecOps enables organizations to meet international regulatory standards through built-in Compliance as Code (CaC) and Infrastructure as Code (IaC) policies. It supports frameworks such as GDPR, PCI-DSS, OWASP, and SOC 2, enabling continuous compliance and reducing the risk of audit failures or penalties.

End-to-End Vulnerability Management:

With full lifecycle visibility, Tx-DevSecOps facilitates proactive identification, tracking, and remediation of vulnerabilities. It uses the Common Vulnerability Scoring System (CVSS) to prioritize risks effectively and eliminate duplicates, streamlining the remediation process and helping teams focus on priority tasks.

Benefits of Implementing Tx-DevSecOps

Accelerated DevSecOps Adoption:

Tx-DevSecOps helps businesses align their secure DevOps initiatives by providing a pre-built, scalable framework that integrates seamlessly into existing DevOps workflows.

Faster, High-Quality Releases:

By automating security checks and aligning DevSecOps best practices across the SDLC, Tx-DevSecOps ensures faster and more frequent releases without compromising quality. It supports effective CI/CD implementation, enabling rapid feature delivery and significantly reducing time to market.

Enhanced Productivity and Time Savings:

Organizations can save up to 25–30% of their time by leveraging the process visibility, automation, and streamlined workflows that Tx-DevSecOps enables. This leads to more efficient project execution and quicker decision-making.

Improved Code Quality and Security:

Tx-DevSecOps incorporates automated testing and security validations, improving code quality and reducing vulnerabilities. With comprehensive metrics and monitoring, teams can continuously enhance their security posture and application stability.

Minimized Manual Effort:

Automating security and compliance tasks across all development pipelines reduces the need for manual intervention. This allows teams to focus more on innovation and delivery while handling routine validations efficiently.

Continuous Monitoring and Feedback:

With real-time monitoring and integrated feedback loops, Tx-DevSecOps provides visibility into build, deployment, and runtime performance. This helps teams quickly detect and resolve issues, optimize reliability, and maintain consistent system health.

Early Involvement of Security Experts:

Security is embedded from the planning phase onward, ensuring that potential risks are identified and mitigated early in the cycle. This proactive approach reduces costly rework and enhances long-term software resilience.

Culture of Shared Responsibility:

Tx-DevSecOps supports collaboration by aligning development, operations, testing, and security teams. With shared goals and responsibilities, teams can deliver reliable, secure software faster and more consistently.

Policy and Compliance Agility:

Customizable pipelines and easy tool integrations simplify enforcing up-to-date security and compliance policies. Organizations stay aligned with evolving standards and regulations such as GDPR, OWASP, and PCI-DSS.

Summary

Tx-DevSecOps is a security automation accelerator designed to integrate robust security practices into the DevOps lifecycle without slowing development. As an extension of Txs’ Tx-DevOps framework, it addresses rising cyber threats by embedding security early using a shift-left approach. Tx-DevSecOps offers tool-agnostic integration, automated SCA, SAST, DAST checks, and compliance management to meet global standards. It enhances productivity, accelerates release cycles, and minimizes manual effort through continuous monitoring and feedback. By enabling collaboration and early risk mitigation, Tx-DevSecOps assists businesses in delivering secure, high-quality software quickly while maintaining a strong security posture and regulatory compliance.

The post Tx-DevSecOps – Bridging the Gap Between Security and Speed in DevOps first appeared on TestingXperts.

1. What is NG-TxHyperAutomate?
2. The Need for Low-Code Test Automation
3. Key Features of NG-TxHyperAutomate
4. Business Benefits of Implementing NG-TxHyperAutomate
5. Real-World Impact: How NG-TxHyperAutomate Improved Test Coverage by 90%
6. Summary

There was a time when enterprises depended on IT professionals to create scripts to keep their processes operational. Traditional QA methods often exceeded development time, as creating a new script takes much longer. However, enterprises must develop a cost-effective test strategy to balance speed and reliability to stay competitive in today’s AI-driven tech environment. That’s where low-code/no-code automation frameworks like NG-TxHyperAutomate come into play. These frameworks offer a better way to automate test scripts with minimal to no coding skills required. Even non-technical users can leverage such a platform to speed up the testing process.

With a goal of making the QA process faster and more reliable, enterprises are turning to AI-driven test automation to achieve better time to market without compromising on quality.

What is NG-TxHyperAutomate?

NG-TxHyperAutomate is an in-house low-code automation framework by Tx ACoE (Automation Center of Excellence) that assists QA teams in accelerating the test automation process. It easily integrates with tools like UiPath Test Suite, Katalon, and Tricentis Tosca, as well as CI/CD pipelines and test management systems like JIRA. The accelerator supports automation across web, mobile, desktop, and API platforms, enabling QA teams to reduce manual efforts and improve regression test coverage.

NG-TxHyperAutomate accelerates software QA processes, achieving faster time to market and maintaining quality standards. By integrating with existing tools and supporting a continuous testing approach, teams can adapt to changing user expectations and evolving business requirements.

The Need for Low-Code Test Automation

As digital transformation accelerators in the AI-driven ecosystem, enterprises must launch high-quality software more frequently and faster. Traditional test automation (like using tools and scripts, requiring coding knowledge) often requires deep technical expertise and time-consuming scripting. It slows down testing cycles and limits the chances of scalability. Low-code/no-code automation frameworks address these obstacles by enabling non-technical users to create test cases and making automation more accessible across teams.

NG-TxHyperAutomate in this space helps simplify and speed up the QA team’s automation processes. It generate automated scripts from manual test cases and offers reusable libraries and built-in support for localization. This helps reduce testing time and ensure flexibility, scalability, and alignment with changing business needs.

Key Features of NG-TxHyperAutomate

Low-Code/No-Code Automation: Enables faster test creation without deep coding knowledge.

Automate Test Generation: Automatically converts manual test cases into automation scripts.

Cross-Platform Coverage: One unified platform supports Web, Desktop, API, and Mobile automation.

CI/CD and Tool Integration: Seamlessly integrates with tools like UiPath Test Suite, Katalon, Tricentis Tosca, and systems like JIRA.

Globalization & Localization Support: Built-in capabilities to test software for multiple languages, regions, and cultures.

Continuous Testing Enablement: Facilitates ongoing validation throughout the software development lifecycle (SDLC).

Reusable Assets & Libraries: Upscale automation efficiency with prebuilt components.

Flexible Data Handling: Allows data to be processed directly from the SQL Server.

Adaptable to Evolving Requirements: Ensures tests align with shifting customer expectations and business needs.

Complex Scenario Handling with Robots: Uses robotic automation for intricate test workflows.

Business Benefits of Implementing NG-TxHyperAutomate

Accelerated Time-to-Market:

By automating test case generation and execution rapidly through low-code/no-code workflows, teams can release software faster and more frequently without degrading quality.

Decreased Testing Costs:

Less manual effort, fewer resources required, and prebuilt automation libraries reduce overall testing and QA-related costs.

Improved Test Coverage and Reliability:

With up to 96% regression coverage and built-in test generation, businesses gain confidence in their releases and minimize post-deployment issues.

Scalable Automation Across Platforms:

A single framework supports Web, Mobile, API, and Desktop testing, enabling consistent automation practices across diverse application ecosystems.

Better Integration with Agile and DevOps:

Integration with CI/CD pipelines, test management tools, and defect tracking systems supports agile development cycles and continuous delivery goals.

Optimized Flexibility and Adaptability:

The system easily accommodates new features, evolving requirements, and third-party integrations, making it ideal for dynamic, enterprise-grade environments.

Lower Maintenance Costs:

Automated maintenance and reusable components help keep test suites up to date with minimal effort as applications evolve.

Risk Mitigation via Early Bug Detection:

Continuous testing throughout the development lifecycle enables earlier defect detection, reducing costly downstream fixes.

Support for Global Expansion:

Built-in localization and globalization testing ensure products are validated for multiple languages and regions, supporting international growth.

Real-World Impact: How NG-TxHyperAutomate Improved Test Coverage by 90%

A major quick-service restaurant chain leveraged NG-TxHyperAutomate to modernize its POS systems globally. Facing bottlenecks due to prolonged manual testing cycles and complex localization needs, the client implemented NG-TxHyperAutomate using UiPath Test Suite alongside additional Tx accelerators. This integration enabled low-code automation, significantly improving deployment efficiency. It helped the client achieve a 150% increase in POS rollout speed, 93% regression test automation, and a 90% boost in overall test coverage, delivering over $1.2 million in QA cost savings.

Summary

Traditional QA approaches struggle to meet the demands of modern software delivery, where speed, scale, and adaptability are critical. NG-TxHyperAutomate addresses this gap with its low-code automation framework that simplifies complex testing processes while enhancing accuracy and coverage. Its ability to support multiple platforms, adapt to localization needs, and integrate into CI/CD pipelines makes it a strategic enabler for quality at speed.

Partnering with Tx for NG-TxHyperAutomate will help you scale automation across platforms, adapt quickly to changing business needs, and optimize your DevOps workflows. Contact our experts now to learn more about how our accelerators can help you optimize your QA processes.

The post Unlock Seamless Test Automation and Drive Quality with NG-TxHyperAutomate first appeared on TestingXperts.

1. What is NG-TxAutomate?
2. Why Businesses Need a Test Automation Framework?
3. Challenges Addressed by NG-TxAutomate
4. Key Features of NG-TxAutomate
5. Summary

It hasn’t been too long since the world witnessed the magic of the Metaverse at the Sandbox’s first Global Creators’ Day on November 3, 2023. As technology expands, enterprises will need more robust digital assurance and automation solutions to survive the era of AI transformation. There’s no denying that Artificial Intelligence (AI) and Machine Learning (ML) are upscaling testing strategies, facilitating the new era of innovation and efficiency.

However, various QA managers or test engineers still hesitate to adopt next-gen technologies to upscale automated testing due to a change in knowledge base and high upfront cost. To overcome such challenges, Tx has an AI-powered framework, NG-TxAutomate, that transforms the quality assurance process and delivers significant QA cost savings.

What is NG-TxAutomate?

NG-TxAutomate is an AI-powered and intelligent “ready-to-deploy” test automation framework by Tx ACoE (Automation Center of Excellence). It is a modular, reusable, compatible, and library-agnostic automation framework that can accelerate the QA process in Agile and DevOps environments. This framework can easily integrate with tools like JIRA, Selenium, Playwright, Jenkins, etc., to support automation efforts across web, mobile, and API platforms. It leverages AI to improve software testing cycles’ accuracy, productivity, and efficiency. Using NG-TxAutomate, enterprises can reduce testing cycle times, lower QA costs, and deliver high-quality software quickly.

QA teams can easily integrate NG-TxAutomate’s AI features into their workflow by adding a plugin to their preferred IDE (Visual Studio Code, IntelliJ IDEA, or another tool). This smooth integration lets them access powerful AI-driven automation within their development environment, boosting productivity and testing efficiency.

Why Businesses Need a Test Automation Framework?

A test automation framework enables businesses to improve software QA processes’ speed, accuracy, and efficiency. It helps deliver faster time to market, improved product quality, and reduced QA costs by automating redundant tasks and enabling teams to focus on complex and AI-driven QA activities. Here’s how it benefits businesses in the long run:

• Automated test cycles enable teams to execute many test cases, including edge cases missed in manual testing.

• Frees up QA engineers from repetitive tasks and gives them time to focus on next-gen testing strategies and analysis.

• A robust framework like NG-TxAutomate significantly reduces the cost of maintaining and updating test scripts.

• Automation framework integrates seamlessly with CI/CD pipelines, enabling continuous testing of code updates throughout the SDLC and rapid deployment.

• Ensuring output consistency across various platforms and environments enables QA teams to execute the same testing steps precisely every time.

Challenges Addressed by NG-TxAutomate 

Key Features of NG-TxAutomate

Speed up QA Efforts Across Platforms:

Run test cases smoothly on web, mobile, and API interfaces without switching between tools.

Easy to Integrate with Tools:

Work efficiently with systems like Playwright, JIRA, Selenium, Azure DevOps, Jenkins, and Cypress without extra setup (hardware/software).

Reduce Repetitive Work:

Teams can reuse test components and scripts, allowing them to spend less time rewriting the same steps.

Catch Bugs Early and Often:

Integrate automated test cases into the software development flow to facilitate quick issue identification.

AI-Powered Capabilities:

AI-driven code generation allows teams to decrease dependency on coding everything from scratch and support extensive reporting via AI insights and predictive models.

Hybrid Testing Support:

Run both functional and non-functional tests across multiple environments to ensure full QA coverage.

Integration with Tx Products:

Easy to integrate with tools like Tx-SmarTest, Tx-Insights, and Tx-GPT to prioritize AI-driven testing, track real-time outputs, and auto-generate test cases from user stories.

Multi-Language Support:

Teams can work with multiple languages, such as Java, C#, Python, and JavaScript, making it easier to build, customize, and maintain test scripts.

Real-world Impact: How NG-TxAutomate Cut Testing Efforts by 90%

In a notable project, Tx worked with a leading commercial insurance provider who was migrating from the legacy systems to the Majesco Insurance Platform. During migration, the client encountered several challenges validating domain-specific functionalities and ensuring application stability in the SIT environment. Tx leveraged NG-TxAutomate, its in-house automation framework, to address these issues and streamline the QA process. It led to a 90% reduction in testing efforts and a 30% increase in test execution speed. The integration of NG-TxAutomate enhanced testing efficiency and ensured functional stability during the platform migration, demonstrating its effectiveness in complex enterprise environments.

Summary

As enterprises race to keep up with the AI transformation, the pressure to deliver faster, smarter, and more reliable software is higher. Yet many enterprises still struggle with outdated tools, steep automation learning curves, and rising costs. NG-TxAutomate, an AI-powered, ready-to-deploy framework from Tx, helps streamline testing, enabling early automation and effortlessly integrating across platforms and tools. With NG-TxAutomate, organizations can confidently adopt digital agility while minimizing QA bottlenecks and boosting release speed.

The post From Weeks to Days – How NG-TxAutomate Shrinks Automation Timelines first appeared on TestingXperts.

1. Understanding a Cloud ERP Solution
2. On-Premises ERP vs Cloud-based ERP Solution
3. Cloud ERP Software Solution Components
4. 5 Types of Cloud ERP Software
5. Why Does Your Business Need a Cloud-based ERP Solution?
6. How can Tx help ensure the Quality of Your Cloud ERP Solution?
7. Summary

Today’s businesses want to scale and expand their operations with the changing technology trends. It’s no secret that firms wish for more than just expansion. They want technologies that are flexible, easy to scale, quick to respond, and unbreakable under strain. Cloud ERP solution is one such technology that enables firms to control their operations and scale their business under a single umbrella. No more isolated departments, complicated software, and outdated task enablers for business activities.

Cloud-based ERP systems are transforming how enterprises run their businesses and adapt to new conditions to deliver long-term value. These systems enable organizations to access the software via the internet anywhere, anytime. All they need is a stable connection and a reliable browser.

Understanding a Cloud ERP Solution

Cloud ERP is an internet-based enterprise resource planning software solution that gives businesses access to essential applications without time and location constraints. It is highly scalable and easy to integrate and upgrade. Some consider it a backbone of the IT infrastructure as it offers advanced functionalities to manage the organization’s core operations. A cloud-based ERP solution is hosted on a provider’s computing platform and is delivered as a “software-as-a-service ERP.” This means businesses don’t directly own the software; instead, they use it on a subscription basis.

In a report, IDC said that the demand for cloud ERP solutions will grow steadily due to their potential to access and analyze large datasets in real time. A cloud ERP system is always online, and the service provider handles the security updates. It decreases the total cost of ownership compared to traditional ERP systems hosted on on-premises servers. Cloud ERPs are also accessible via mobile devices and desktop computers. Enterprises can integrate various business applications to keep themselves running efficiently as they work towards improving the customer experience and delivering value-driven business outcomes.

On-Premises ERP vs Cloud-based ERP Solution

Cloud ERP Software Solution Components

A cloud-based ERP software offers financial and accounting features at its core. The type of applications, modules, and features a company selects to leverage depends on its business needs and industry type. Let’s look at the core components of cloud ERP that integrate various business functions, streamline operations, and provide a unified platform for data management and workflow automation:

Financial and Accounting:

Handles budgeting, accounts payable and receivable, and financial reporting necessary to manage the money-related aspects of a business.

HRM/HCM:

Focuses on workforce handling, including payroll, employee benefits, talent acquisition, and other HR-related processes.

Supply Chain Management:

Deals with planning, coordinating, and executing the flow of data, goods, and funds related to the supply chain.

Customer Relationship Management:

Helps manage customer interactions, including contact management, sales forecasting, customer support, and lead tracking.

Business Intelligence:

Provides data analytics and reporting functionalities, enabling enterprises to gain insights into performance and make informed decisions.

Other common modules in a cloud ERP software solution include order management, manufacturing, warehouse management, and inventory management.

5 Types of Cloud ERP Software

One thing everyone should know is that not all clouds are equal. Businesses that have retrofitted their legacy ERP software via their internet-connected data centers may miss out on the benefits of a cloud ERP solution. Instead of such an approach, there are multiple cloud-based ERP software that businesses can utilize based on their requirements:

Single-Tenant SaaS:

This ERP software solution serves only one organization. The data is hosted on private servers that run a single version of this ERP software. Cloud ERP vendors can also allow clients to run it privately or in a shared space.

Multi-Tenant SaaS:

This type of ERP solution serves multiple organizations simultaneously. Although organizations may use the same software on the same server, their data will remain protected and secure from others.

Public Hosted:

The service provider owns this cloud-based ERP solution, and multiple enterprises share the computing space. Despite this, each enterprise’s data and applications remain inaccessible to others. Public cloud examples include Google Cloud, Oracle Cloud, and AWS.

Private Hosted:

The ERP software is hosted on an enterprise private server. It offers maximum control and security for data and applications, but requires initial infrastructure investment.

Hybrid Cloud:

This ERP solution combines on-premises software with private or public cloud for services, storage, and computing.

Why Does Your Business Need a Cloud-based ERP Solution?

• Avoid hefty upfront costs associated with traditional ERP hardware and software setup.

• Its subscription-based model is affordable and manageable for businesses of all sizes.

• No need to maintain costly on-site traditional ERP infrastructure.

• Easy to scale as your business grows without complex upgrades.

• Add/remove users and expand functionality per your requirements with minimal disruption.

• Access the application/services anywhere, anytime, with an internet connection.

• Real-time access to data and decision-making capabilities.

• Up-to-date visibility across financial, sales, inventory, and other segments.

• Helps make quicker, data-driven decisions based on real time information.

• The service provider handles the software updates and maintenance.

• Break down departmental silos between HR, sales, and finance.

• The team can share information more effectively and enhance productivity with streamlined communication.

• Cloud providers invest in strong security measures, including encryption and regular backups, ensuring higher security than on-premises solutions.

• Facilitate easy integration with CRM, marketing tools, HR, and other software systems.

• Easy to store and restore data in the event of disruption.

• Track orders, manage communications, and deliver personalized services.

• Flexible and cost-effective, enabling businesses to adapt to market changes quickly.

How can Tx help ensure the Quality of Your Cloud ERP Solution?

Thousands of enterprises globally are using cloud ERP solutions, which enable businesses to fast-track their transformation efforts. However, despite this, they need a QA solution to ensure seamless implementation of a cloud-based ERP system. Tx, the leading vendor in digital assurance and quality engineering services, helps you ensure the quality and reliability of your cloud ERP solutions.

• Our comprehensive functional and performance testing ensures that every aspect of your cloud-based ERP solution is secure, robust, and high-performing.

• We help protect your business data by conducting security assessments, compliance checks, and penetration testing.

• Our experts rigorously test ERP integration with CRM, payroll, and other essential platforms to ensure error-free data flow across your enterprise ecosystem.

• We leverage automation frameworks to ensure new updates or changes do not break existing functionality, keeping your ERP system stable and dependable.

Summary

Cloud ERP solutions redefine how businesses grow by offering flexibility, real-time access, and seamless operations across departments. With lower upfront costs and easy scalability, cloud-based ERP empowers organizations to make faster, data-driven decisions while enhancing collaboration and security. Tx helps ensure the reliability of these solutions through comprehensive testing services, including functional, performance, security, and integration testing. We help our clients confidently adopt and optimize cloud ERP systems, supporting smooth operations and sustainable growth. To know how Tx can help, contact our experts now.

The post Scaling Smarter with Cloud ERP Solution – Driving Business Growth first appeared on TestingXperts.

Artificial Intelligence (AI) is taking quality assurance (QA) to a new horizon. According to sources, the market size of AI in the quality assurance domain will grow at a CAGR of 35.7% by 2030, making it a $1,339.1 billion industry. The value AI brings by optimizing QA processes and improving efficiency can’t be ignored by businesses that want to scale with the new tech trends. Enterprises that know the value of AI-driven QA are on the path to gaining a strong competitive advantage.

AI tools are fast to act, detect patterns/anomalies missed by human testers, and can simulate user behaviors, delivering a comprehensive view of software performance. Companies can fast-track their development cycles, improve software quality and reliability, and deliver high-performing products to increase customer engagement.

Understanding the Role of AI in Software Quality Assurance

Software testing is a long, time-constrained task involving test suite development, test data generation, and test case management. Manual testing is prone to errors, which, in turn, increases costs and extends project delivery timelines. Leveraging AI in quality assurance can address the drawbacks of traditional testing and can expedite the whole process. AI can undertake various tasks that were previously handled by manual testers. The following are the technologies that will be involved here:

Natural Language Processing (NLP):

It involves understanding, interpreting, and responding to human language while considering linguistic exceptions. This means AI can read and decrypt QA requirements written in plain language and convert them into test cases or automation scripts.

Computer Vision:

It analyzes and interprets visual data and detects anomalies in the UI, allowing QA teams to perform more accurate visual regression testing.

Machine Learning:

ML models enable AI systems to learn from data and past experiences without programming. QA teams can train their AI models by leveraging their testing data, and they can fine-tune the test data patterns and recommendations, making it organization specific.

Also, leveraging AI in the quality assurance processes would take a closer approach to autonomous testing.

Roadmap to AI-driven Autonomous Testing

Stage 1: No QA: Minimal or no testing is performed.

Stage 2: Ad hoc Testing: Informal testing processes handled by individuals.

Stage 3: Manual Testing: Manual testers execute structured test cases.

Stage 4: Test Automation: Test scripts automate repetitive tests and run without human supervision.

Stage 5: AI-Powered Testing: AI generates and maintains test scripts and assists testers.

Stage 6: Autonomous Testing: AI is responsible for the entire QA process, from creating test cases to executing them and analyzing the results.

The journey to autonomous testing can be seen as how QA maturity scales in multiple stages. The above-mentioned flow represents the evolution of QA, from no testing to self-driven AI-enabled testing. This shows how enterprises rely heavily on AI and modern technology, with each step highlighting improvement in accuracy, value delivery, and scalability. Enterprises generally go through these stages to reduce their manual QA efforts and improve test case coverage and product quality as their business grows.

Manual Testing Vs AI-driven Autonomous Testing

Future of AI-driven Autonomous Testing in Software Quality Assurance

The software testing industry is already on the path to autonomy. The currently established automation testing approach is moving towards AI-driven autonomous testing, although it’s still only in the infant stage. AI technologies need time to get trained and integrated into a business infrastructure. So, it will take some time, but eventually, enterprises will recognize the complete benefits of leveraging AI in quality assurance. The following are some of the potential aspects:

AI-Powered Test Case Generation:

After analyzing and comprehending an enterprise’s testing requirements, AI can suggest test cases and generate test scripts by itself. However, the QA teams must feed AI with quality data and testing sessions to make this possible.

Autonomous Test Orchestration:

By granting AI access to real-time organizational test data, businesses can manage E2E testing processes seamlessly. Teams can automate test case scheduling according to available resources, make accurate decisions for test execution, and streamline the whole QA environment.

Self-Healing Tests:

Autonomous test scripts adapt in real time without breaking when UI elements or APIs are updated. Teams will use AI-powered locators and pattern recognition algorithms to identify equivalent endpoints depending on context or historical data.

AI-Powered Visual Testing:

There’s an issue with the automation test scripts, as they might throw false positives. AI-powered visual testing will help mitigate this issue by better detecting true visual bugs that might have a negative impact on the user.

DevOps Integration:

Autonomous testing is rapidly being implemented into DevOps practices to create a seamless, continuous integration pipeline for Dev, QA, and release. Quality checks will be integrated at every step, enabling enterprises to release products much faster and with higher assurance.

How Can Tx Assist You With AI-Driven Autonomous Testing?

Transformation demands more than speed. It requires intelligence, accuracy, and resilience. Businesses nowadays require a QA solution that is fast, scalable, and can evolve. AI-driven autonomous quality assurance can be all of it. The collaboration between AI and human intelligence would deliver more comprehensive and effective testing solutions. At Tx, we can assist you in unlocking the perks of using AI in quality assurance processes by enabling autonomous software testing.

By leveraging AI/ML-driven practices, we can help you speed up your QA process in the DevOps chain and grow your business. We combine human expertise with AI capabilities to optimize test coverage and automate your QA lifecycle. Our advanced AI-based NGAutomate and NGHyperAutomate accelerators enable flawless and faster application releases while considering excellent quality and customer satisfaction.

Summary

AI is transforming software testing by enabling autonomous testing, where systems can create, run, and adapt tests without manual input. This reduces human error, improves test coverage, and keeps pace with rapid development changes. From identifying bugs to updating tests automatically, autonomous testing streamlines the entire QA process. Integrated with development workflows, it helps deliver reliable software faster. With expert support and AI-powered tools, Tx helps its clients adopt autonomous testing for better performance and efficiency. To learn how Tx can assist you, contact our AI experts now.

The post Role of AI-driven Autonomous Testing in Software QA first appeared on TestingXperts.

The current state of AI advancements can be termed an unprecedented moment, as it rapidly transforms industries, with general-purpose AI (GPAI) models like Gemini and ChatGPT leading the change. However, the advancement also brings significant challenges. As per statistics, around 80-85% of AI projects fail to meet goals due to poor data quality, insufficient risk controls, and many other issues. After understanding these challenges, regulatory bodies are coming forward to ensure responsible and ethical AI development and deployment. The EU Artificial Intelligence Act (AI Act) introduces various guidelines for GPAI models, especially posing systemic risks.

This blog will discuss the criteria for identifying GPAI models having systemic risks, EU AI regulatory requirements for such models, and how AI models can comply with evolving regulations.

What are General-Purpose AI (GPAI) Models?

The EU AI Act defines GPAI models as AI models trained using large data volumes and self-supervision at scale. These models can perform multiple tasks across domains without depending on a particular functionality. General-purpose AI models can adapt and be fine-tuned to support different applications, including content creation, coding, translation, data analytics, and decision-making. Generally, there are three types of GPAI models:

Foundational Models

For example, GPT-4, Gemini, and Llama, trained on large-scale data and fine-tuned for downstream tasks.

Multimodal Models

For example, models that can generate text, image, audio, etc., by handling and integrating multiple types of input.

Instruction-Tuned Models

Fine-tuned to follow human instructions across general tasks.

But, under the EU AI Act, more critical GPAI Model types have emerged:

GPAI Models Without Systemic Risk

Although these models are broadly capable of performing diverse tasks like code assistance, content creation, and translation, they do not fulfill the criteria of systemic impact. They still need to follow transparency and usage guidelines defined by regulatory bodies but are not subject to the highly critical obligations under the law.

GPAI Models with Systemic Risk

These powerful models significantly impact the economy, business operations, and society. If they are left unchecked or unregulated, the aftereffects would be severe. Systemic risks may emerge from:

Model scale and capabilities

Trained on extremely large datasets, the models gain advanced autonomy and can generate compelling content, making them harder to predict or control. For instance, GPT-4 or Gemini can write software code, simulate human conversations, or generate legal documents.

Deployment Reach

Recently, organizations have been integrating GPAI models with their critical systems to multiply their impact. For instance, a general-purpose model merged with a cloud platform, a financial service tool, or a government chatbot will significantly impact millions of users if it generates biased or harmful outputs.

Risk of Misuse

The more advanced the model, the higher the risk of being used unethically and maliciously. Malicious actors can use it to create misinformation, deepfakes, or exploit security gaps within an infrastructure. For instance, bad actors might use an AI model to make phishing emails, spread misinformation during a nationwide event, or manipulate stock markets.

GPAI Models Without Systemic Risk

Although these models are broadly capable of performing diverse tasks like code assistance, content creation, and translation, they do not fulfill the criteria of systemic impact. They still need to follow transparency and usage guidelines defined by regulatory bodies but are not subject to the highly critical obligations under the law.

GPAI Models with Systemic Risk

These powerful models significantly impact the economy, business operations, and society. If they are left unchecked or unregulated, the aftereffects would be severe. Systemic risks may emerge from:

Model scale and capabilities

Trained on extremely large datasets, the models gain advanced autonomy and can generate compelling content, making them harder to predict or control. For instance, GPT-4 or Gemini can write software code, simulate human conversations, or generate legal documents.

Deployment Reach

Recently, organizations have been integrating GPAI models with their critical systems to multiply their impact. For instance, a general-purpose model merged with a cloud platform, a financial service tool, or a government chatbot will significantly impact millions of users if it generates biased or harmful outputs.

Risk of Misuse

The more advanced the model, the higher the risk of being used unethically and maliciously. Malicious actors can use it to create misinformation, deepfakes, or exploit security gaps within an infrastructure. For instance, bad actors might use an AI model to make phishing emails, spread misinformation during a nationwide event, or manipulate stock markets.

Identifying the GPAI Model with Systemic Risk

General-purpose AI Models containing systemic risk will greatly impact everyone’s lives and business operations. The impact would be so significant that any negative AI incident could disturb the whole technology value chain, disrupt business operations, and impact end-users who rely on it. That’s how GPAI models are categorized into the systemic risk category. Now, the question is, “How do you calculate the impact capability?”

The high impact of any AI model is identified when its cumulative computing power utilized during its training is greater than 10²⁵ Floating Point Operations (FLOPS). FLOPS/second is a unit that calculates a computer’s processing speed. A higher FLOPS value means higher power consumption, which also increases the risk factor. Also, FLOPS is not the only performance indicator in the EU Commission’s AI office. They will set additional benchmarks to identify and assess systemic risk.

The AI Office will now supervise and implement the laws listed in the AI Act for GPAI Model providers (Article 88 AI Act). The AI Office will work alongside national authorities in EU countries to help them check if AI systems meet the required standards. It can ask companies to provide information about their models, carry out evaluations, and, if needed, demand changes or even remove a model from the market entirely. If GPAI model providers don’t comply, the Office can issue fines of up to 3% of a company’s worldwide annual revenue or 15 million euros, whichever is higher.

Regulatory Requirements for GPAI Models Posing Systemic Risk

Providers of GPAI models identified as posing systemic risk must adhere to the following obligations:

Risk Assessment and Mitigation

Conduct thorough evaluations of their models using standardized protocols and tools that reflect the current state of the art. This includes adversarial testing to identify and mitigate systemic risks.

Incident Reporting

Monitor serious incidents and report relevant information, including possible corrective measures, to the AI Office and, where necessary, to national competent authorities immediately.

Cybersecurity Measures

Ensure adequate cybersecurity for the model and its physical infrastructure to prevent unauthorized access and other security threats.

Compliance Demonstration

Until conformed standards are published, providers can use codes of practice to demonstrate compliance with the AI Act’s requirements.

Transparency and Documentation

Maintain detailed records of the model’s development and testing processes. Provide necessary information to downstream providers integrating the model into their AI systems while safeguarding intellectual property rights.

How Does Tx Ensure Your AI Models’ Compliance with AI Regulations?

With the increasing adoption of AI technologies, a critical gap also emerges ensuring AI models’ reliability, authenticity, ethics, and responsibility. At Tx, we understand these challenges and lead the way in AI quality engineering by deep diving into your general-purpose AI models’ functionalities. Our years of experience in ensuring compliance with region-specific regulatory requirements enable our clients to build GPAI AI models that are robust, secure, trustworthy, and scalable.

General-purpose AI models, like GPT-4 and Gemini, are increasingly powerful and widely used, but they also pose risks when left unchecked. The EU AI Act sets strict rules for models with systemic impact—those using massive computing power or influencing critical sectors. These rules include mandatory risk assessments, incident reporting, and cybersecurity measures. The AI Office enforces these obligations. Tx supports organizations by helping ensure their AI models meet compliance standards through in-depth testing, governance, and responsible deployment practices. Contact our AI QE experts now to find out how Tx can assist with your AI projects.

The post How General-Purpose AI (GPAI) Models Are Regulated first appeared on TestingXperts.

In the rapidly evolving insurance ecosystem, balancing regulatory changes with dynamic customer needs is one of the primary challenges. The process requires a combination of accuracy and efficiency, which AI Agents focus on implementing. AI is the next-gen technology in the artificial intelligence domain, and it is reshaping how insurance organizations work and engage with their users. AI agents in the insurance industry utilize ML algorithms to analyze massive datasets faster and more accurately.

Insurers can leverage these agents to make informed decisions with high precision, upscaling the underwriting process, enabling claim process automation, and improving operational flow. This blog takes a deep dive into the role of AI agents in insurance, its use cases, and the benefits that insurers get with this technology.

Understanding AI Agents’ Role in Insurance

The insurance industry faces challenges such as information overload, changing customer demands, risk management, and the complexities of modern technology infrastructure. AI agents in insurance work as advanced software systems that use ML, NLP, and predictive analytics to upscale, enhance, and automate various components of the insurance business. These systems streamline operations and improve CX by offering customers services and speeding up response time.

Agentic AI’s risk assessment capabilities enable insurers to handle complex scenarios and mitigate potential threats. They can analyze data more efficiently, automate decision-making, and predict outcomes quickly and precisely. As a result, they can streamline operations and improve CX by offering customized services and quick responses.

AI Agents’ key functions in insurance involve:

Risk Assessment:

AI agents leverage predictive modeling to analyze extensive datasets, such as market trends, customer profiles, and claims data history, for detailed risk evaluation.

Policy Underwriting:

AI agents assist with underwriting tasks by evaluating risks related to insuring entities or individuals.

Claim Processing:

AI Agents automate data collection, verification, and preliminary assessments, ensuring customers receive accurate support.

Fraud Detection:

AI Agents recognize patterns in claims and transactions to spot fraudulent activities and adapt to new fraud tactics by continuously learning.

AI Agents Key Components for Insurance

AI Agents’ architecture consists of components that support input data processing, action planning, reasoning, and data execution depending upon business requirements. Its key components include:

Natural Language Processing (NLP):

Agents can understand and respond to user queries in plain and simple language via chatbots, voice calls, or email, further enhancing customer engagement and automating claims inquiries.

ML Models:

Assist with risk assessment, fraud detection, claim underwriting, and curating pricing strategy by analyzing multiple data sources and learning from patterns.

Predictive Analytics:

Forecast customer behavior, claim likelihoods, and churn rates, enabling insurers to make better marketing, retention, and risk handling decisions.

Workflow Automation:

Automates routine processes such as claim filing, documentation collection, and policy updates while reducing manual work and turnaround times.

Data Ingestion and Processing Layer:

Classifies and processes structured and unstructured data from multiple sources like claim forms, third-party systems, CRMs, and emails.

Identity and Access Management:

Ensuring protection of customer data and interactions and maintaining compliance with regulations like HIPAA, GDPR, PCI DSS, etc.

Integration with Insurance Processes:

Enable seamless connection with policy admin systems, CRMs, billing systems, and claim management platforms to deliver actionable and real-time insights.

Use Cases of AI Agents in Insurance

Sentiment Analysis:

Evaluate customer sentiment by analyzing social media activity and feedback. This enables insurers to understand their audience preferences better and respond to improve customer engagement and retention rates.

Claim Process Automation:

Autonomous agents align claim handling and processing by automating document verification, damage assessment, and claim approval. Direct claim settlement becomes faster while processing cost becomes lower, and customers get better service as their claims resolve more accurately and quicker.

Credit Scoring:

By leveraging predictive analytics, these agents analyze policyholder credit scoring against their risk profile. This gives a deeper understanding of the risk factors and the borrower’s creditworthiness. The result? Insurers gain the competitive edge and can personalize their products.

Cybersecurity Management:

Autonomous Agents facilitate risk assessment and prevent fraud by spotting pattern misalignment. Insurers can identify and isolate cyber incidents and prevent breaches before they affect their practices, thus effectively managing risks.

Marketing and Sales:

AI agents uplift insurance marketing and sales by analyzing customer data to identify potential leads and target them with personalized campaigns. By understanding preferences, behaviors, and engagement history, these agents support effective lead scoring and help prioritize prospects based on their likelihood to convert.

Automated Policy Renewal:

AI Agents automate renewal reminder messages by analyzing customer renewal cycles and personal information shared when purchasing the policy. This ensures timely communication, reduces the chances of policy lapses, and improves customer retention rates.

Key Benefits of AI Agents in Insurance 

How can Tx Help You Improve Your AI Agents Quality?

As AI Agents’ implementation accelerates, ensuring accuracy, security, and reliability becomes a critical challenge for enterprises. Model drift, biased decision-making, data inconsistencies, and lack of explainability lead to compliance failures, security issues, and inaccurate predictions. At Tx, we lead the way in AI quality engineering with our Agentic approach and years of experience validating AI models, mitigating biases, and ensuring your AI Agents are trustworthy, robust, and scalable. By partnering with Tx, you get access to:

AI Advisory:

Strategic guidance on implementing and scaling AI agents responsibly in insurance workflows, from claims automation to underwriting, while staying compliant with industry regulations.

Advanced AI QE Techniques:

We apply specialized testing methods to validate AI behavior under real-world insurance scenarios, ensuring decisions are accurate, consistent, and explainable across customer segments.

Expert AI Team:

Our experienced data and QA engineers bring deep domain knowledge in insurance, helping to identify and fix edge-case errors, bias in pricing models, and claim prediction risks.

Customized Testing:

We design tailored test strategies based on your insurance product lines, data sources, and customer personas, ensuring your AI agents work reliably across policy types and geographies.

Agentic AI QE:

Our unique Agentic approach enables continuous testing and monitoring of AI agents in production, identifying issues like model drift or degradation before they impact users or compliance.

Summary

AI Agents are transforming the insurance industry by enabling real-time, data-driven decision-making across underwriting, claims, fraud detection, and customer engagement. With advanced ML, NLP, and predictive analytics, these agents streamline operations, improve accuracy, and enhance customer experiences. Tx supports insurers with specialized testing, validation, and quality assurance for trustworthy Autonomous Agents deployment. To learn how our AI experts can assist you, contact us now.

The post Top 5 Use Cases for AI Agents in the Insurance Industry first appeared on TestingXperts.