Test Orchestration Archives

Modern business depends entirely on the integrity of the information flowing through its systems. Poor data quality costs organizations an average of $12.9 million annually, making the choice of validation tools a high-stakes executive decision.

Tricentis Data Integrity stands as the established player. Meanwhile, Qyrus Data Testing emerges as a unified “TestOS” challenger, designed for teams that prioritize full-stack agility and AI-driven efficiency. Qyrus offers a streamlined testing experience with a focus on consolidating Web, Mobile, API, and Data testing into one environment.

The Connectivity Illusion: Why 200 Connectors Might Still Leave You Blind

Volume often acts as a smokescreen for actual utility in the enterprise testing market.

Tricentis commands the lead in sheer breadth, offering a massive library of 50+ SQL connectors and deep, specialized support for SAP systems and Salesforce. This exhaustive reach positions them big in the data connectivity category. Large organizations with legacy-heavy footprints view this as a non-negotiable safety net for complex IT environments.

Data Source Connectivity

Feature	Qyrus Data Testing	Tricentis Data Integrity
SQL Databases
MySQL	✓	✓
PostgreSQL	✓	✓
MS SQL Server	✓	✓
Oracle	✓	✓
IBM DB2	✓	✓
Snowflake	✗	✓
AWS Redshift	✓	✓
Azure Synapse	✗	✓
Google BigQuery	✗	✓
Netezza	✗	✓
NoSQL Databases
MongoDB	✓	✓
DynamoDB	✗	✓
Cassandra	✗	✓
Hadoop/HDFS	✗	✓
Cloud Storage & Files
AWS S3	✓	✓
Azure Data Lake (ADLS)	✗	✓
Google Cloud Storage	✗	✓
SFTP	✗	✓
CSV/Flat Files	✓	✓
JSON Files	✓	✓
XML Files	◐	✓
Excel Files	◐	✓
Parquet	✗	✓
APIs & Applications
REST APIs	✓	✓
SOAP APIs	◐	✓
GraphQL	◐	◐
SAP Systems	✗	✓
Salesforce	✗	✓

Legend: ✓ Full Support | ◐ Partial/Limited | ✗ Not Available

However, the Pareto Principle reveals a different reality for modern data teams.

Research indicates that 80% of enterprise data integration needs require only 20% of available connectors. While platforms like Airbyte offer up to 600 options, the vast majority of high-value workloads concentrate on a “vital few”: MySQL, PostgreSQL, MongoDB, Snowflake, Amazon Redshift, and Amazon S3.

Qyrus focuses its 75% connectivity score exactly on these critical hubs. It masters the SQL connectors and cloud storage platforms that drive current digital transformations.

The integration gap is real. Large enterprises manage an average of 897 applications yet only 29% of them are actually integrated. Qyrus bridges this gap by validating the REST, SOAP, and GraphQL APIs that feed your pipelines. It prioritizes the connections that matter most to your daily operations rather than maintaining a list of nodes you will never use.

Securing the Core: Why Data Validation is the New Standard for Quality

Precision in data validation determines the difference between a high-performing enterprise and a costly financial sinkhole. While connectivity creates the bridge, validation ensures the cargo remains intact. Organizations currently lose a staggering $12.9 million annually due to poor data quality, making advanced testing capabilities more critical than ever.

Tricentis Data Integrity excels in deep-layer requirements like slowly changing dimensions (SCD) and data lineage tracking, which are vital for regulated industries needing to prove data history.

Its “Pre-screening wizard” acts as a high-speed filter, catching structural defects before they enter the processing pipeline. Large, SAP-centric organizations rely on this model-based approach to prioritize risks across complex, multi-layered environments.

Testing & Validation Capabilities

Feature	Qyrus Data Testing	Tricentis Data Integrity
Comparison Testing
Source-to-Target Comparison	✓	✓
Full Data Comparison	✓	✓
Column-Level Mapping	✓	✓
Cross-Platform Comparison	✓	✓
Reconciliation Testing	✓	✓
Aggregate Comparison (Sum, Count)	✓	✓
Single Source Validation	✓	✓
Row Count Verification	✓	✓
Data Type Verification	✓	✓
Null Value Checks	✓	✓
Duplicate Detection	✓	✓
Regex Pattern Validation	✓	✓
Custom Business Logic/Functions	✓	✓
Referential Integrity Checks	◐	✓
Schema Validation	◐	✓
Advanced Testing
Transformation Testing	✓	✓
ETL Process Testing	✓	✓
Data Migration Testing	✓	✓
BI Report Testing	✗	✓
Tableau/Power BI Testing	✗	✓
Pre-Screening / Data Profiling	◐	✓
Data Lineage Tracking	✗	✓

Qyrus Data Testing takes an agile path, focusing on most core validation tasks that drive daily business decisions. It provides unique value through Lambda function support, allowing teams to inject custom business logic directly into its automated data quality checks. This “TestOS” approach bridges the gap between different layers, enabling you to verify that a mobile app transaction accurately reflects in your cloud warehouse. While it currently skips BI report testing, Qyrus offers a faster, no-code route for teams wanting to eliminate the “garbage in” problem at the point of entry.

Precision testing must move beyond simple row counts to secure your strategic truth. If your ETL data testing framework cannot see the logic within the transformation, you are only protecting half of your pipeline.

Beyond the Script: Scaling Quality with Intelligent Velocity

Automation serves as the engine that moves data quality from a reactive chore to a proactive strategy. Organizations that fail to automate their pipelines see maintenance costs consume up to 70% of their total testing budget. Modern teams now demand more than just recorded scripts; they need platforms that think.

Tricentis utilizes a model-based approach that decouples the technical steering from the test logic, allowing for resilient automation that doesn’t break with every UI change. With over 100 API calls and native support for the entire SAP ecosystem, it fits seamlessly into the most rigid enterprise CI/CD pipelines. Its “Pre-screening wizard” further accelerates the process by identifying early data errors before heavy testing begins.

Automation and Integration

Feature	Qyrus Data Testing	Tricentis Data Integrity
Test Automation
No-Code Test Creation	✓	✓
Low-Code Options	✓	✓
SQL Query Support	✓	✓
Visual Query Builder	✓	✓
Test Scheduling	✗	✓
Reusable Test Components	✓	✓
Parameterized Testing	✓	✓
AI/ML Capabilities
AI-Powered Test Generation	✓	✓
Auto-Mapping of Columns	✓	✓
Self-Healing Tests	◐	✓
Generative AI for Test Cases	✓	✓
DevOps/CI-CD Integration
REST API	✓	✓
Jenkins Integration	✗	✓
Azure DevOps	✗	✓
GitLab CI	✗	✓
GitHub Actions	✗	✓
Webhooks	◐	✓
Issue & Test Management
Jira Integration	✓	✓
ServiceNow Integration	◐	✓
Slack/Teams Notifications	✓	✓
Email Notifications	✓	✓

Qyrus Data Testing counters with a heavy focus on democratization through Nova AI. This intelligent engine automatically generates testing functions and identifies data patterns, helping teams build test cases 70% faster than manual methods. Qyrus emphasizes a “no-code” philosophy that allows manual testers to contribute to the ETL data testing framework without learning complex coding languages. It integrates directly with Jira, Jenkins, and Azure DevOps to ensure that automated data quality checks remain part of every code push.

True velocity requires a platform that minimizes technical debt while maximizing coverage. Whether you lean on Tricentis’ enterprise-grade models or Qyrus’ AI-powered speed, your ETL testing automation tools must remove the human bottleneck from the pipeline.

The Digital Mirror: Transforming Raw Data into Strategic Intelligence

Visibility acts as the final safeguard for your information integrity. Without robust analytics, even the most sophisticated automated data quality checks remain silent. Organizations that lack transparent reporting struggle to identify the root cause of data corruption, often treating symptoms while the underlying disease persists.

Tricentis Data Integrity secures a perfect score for reporting and analytics. It provides deep-drill analysis that allows engineers to trace a failure from a high-level dashboard down to the specific row and column. This platform excels at Root Cause Analysis (RCA), helping teams determine if a failure stems from a physical hardware fault, a human configuration error, or an organizational process breakdown. Furthermore, it offers complete integration with BI tools like Tableau and Power BI, ensuring your executive reports are as verified as the data they display.

Reporting and Analytics

Feature	Qyrus Data Testing	Tricentis Data Integrity
Real-Time Dashboards	✓	✓
Drill-Down Analysis	✓	✓
Root Cause Analysis	◐	✓
PDF Report Export	✗	✓
Excel Report Export	✓	✓
Trend Analysis	◐	✓
Data Quality Metrics	◐	✓
Custom Report Templates	◐	✓
BI Tool Integration (Tableau, Power BI)	✗	✓
Audit Trail	✓	✓

Qyrus Data Testing earns a 72% category score with its modern, real-time approach. Its dashboards focus on “Operational Intelligence,” providing immediate access to KPIs so you can react to changing conditions in seconds. Qyrus emphasizes automated audit trails to ensure compliance without manual paperwork. While its root cause and trend analysis features are currently in Beta, the platform provides the essential visibility needed for high-velocity teams to act with confidence.

A real-time dashboard is not just a display; it is a tool that shortens the time to a decision. Whether you require the deep forensic reporting of Tricentis or the agile, live signals of Qyrus, your data quality testing tools must turn your pipeline into an open book.

Fortresses and Clouds: Choosing Your Infrastructure Architecture

Your choice of deployment model dictates the ultimate control you maintain over your sensitive information. Both platforms offer the flexibility of Cloud (SaaS), On-Premises, and Hybrid deployment models. However, the maturity of their security frameworks marks a significant divergence for regulated industries.

Platform and Deployment

Feature	Qyrus Data Testing	Tricentis Data Integrity
Cloud (SaaS)	✓	✓
On-Premises	✗	✓
Hybrid Deployment	◐	✓
Docker Support	◐	✓
Kubernetes Support	◐	✓
Multi-Tenant	◐	✓
SSO/LDAP	✓	✓
Role-Based Access Control	✓	✓
Data Encryption (AES-256)	✓	✓
SOC 2 Compliance	◐	✓

Qyrus Data Testing earns a strong platform score by prioritizing modern, containerized workflows. The platform fully supports Docker and Kubernetes for teams that want to manage their ETL testing automation tools within a private, scalable infrastructure. It employs AES-256 encryption and Single Sign-On (SSO) for secure authentication. This makes Qyrus an excellent fit for agile, cloud-native organizations that value technical flexibility over legacy certifications.

If your team demands a lightweight, containerized environment that scales with your code, Qyrus provides the modern edge.

The Verdict: Architecting Your Truth in a Data-First World

The decision between Tricentis Data Integrity and Qyrus Data Testing ultimately hinges on the scope of your quality mission. Both platforms eliminate the risk of manual error, but they serve different strategic masters.

Tricentis Data Integrity provides an exhaustive, enterprise-grade fortress. It remains the clear choice for global organizations with complex, SAP-centric landscapes that require every possible certification and deep forensic validation. If your primary goal is risk-based prioritization and you manage a sprawling legacy footprint, Tricentis offers the most complete safety net on the market.

Qyrus Data Testing counters with a vision for total platform consolidation. It functions as a specialized module within a broader “TestOS,” making it the ideal choice for agile teams that need to verify quality across Web, Mobile, and API layers simultaneously. Choose Qyrus if you want to empower your existing staff with AI-powered automation and move from pilot to production in weeks rather than months.

Data quality is not a static checkbox; it is the heartbeat of your digital transformation. Secure your strategic integrity by selecting the engine that matches your operational speed. Whether you need the massive breadth of an enterprise leader or the unified agility of a modern TestOS, stop the $12.9 million drain today.

Secure your data integrity now by starting a 30-day sandbox evaluation.

The gatekeeper model of Quality Assurance just broke. For years, we treated QA as a final checkbox before a release. We wrote static scripts and waited for results. But the math has changed. By 2026, the global testing market will hit approximately $57.7 billion. Looking further out, experts project a climb toward $100 billion by 2035.

We are witnessing a massive capital reallocation. Organizations are freezing manual headcount and moving those funds into intelligent test automation. It is a pivot from labor-intensive validation to AI-augmented intelligence. You see it in the numbers: while the general market grows at roughly 11%, AI trends in software testing show an explosive 20% annual growth rate.

This is more than a budget update. It is a fundamental dismantling of the traditional software development lifecycle. Quality is no longer a distinct phase. It is an intelligence function that permeates every microsecond of the digital value chain.

Autonomous Intent: Leaving the Brittle Script Behind

The era of writing static, fragile test cases is nearing its end. Traditional automation relies on Selenium-based scripts that break the moment a developer changes a button ID or moves a div. This “flakiness” is an expensive trap, often consuming up to 40% of a QA team’s capacity just for maintenance. We are moving toward a future where software testing predictions 2026 suggest the complete obsolescence of these brittle scripts.

Instead of following a rigid Step A to Step B path, we are deploying autonomous agents. These agents do not just execute code; they understand intent. You give an agent a goal—such as “Complete a guest checkout for a red sweater”—and it navigates the UI dynamically. It handles unexpected pop-ups and A/B test variations without crashing. This shift is so significant that analysts expect 80% of test automation frameworks to incorporate AI-based self-healing capabilities by late 2025.

Self-healing tools use computer vision and dynamic locators to identify elements by context. If an element ID changes, the AI finds the button that “looks like” the intended target and updates the test definition on the fly. The economic impact is clear: organizations using these mature AI-driven test automation trends report 24% lower operational costs. By removing the drudgery of maintenance, your engineers finally focus on expanding coverage rather than fixing what they already built.

Intelligent Partners: The Rise of AI Copilots and the Strategic Tester

The narrative that AI will replace the human tester is incomplete. In reality, AI trends in software testing indicate a transition toward a “Human-in-the-Loop” model where AI serves as a force multiplier. Roughly 68% of organizations now utilize Generative AI to advance their quality engineering agendas. However, a significant “trust gap” remains. While 82% of professionals view AI as essential, nearly 73% of testers do not yet trust AI output without human verification.

AI copilots now handle the high-volume, repetitive tasks that previously bogged down release cycles. These tools generate comprehensive test cases from user stories in minutes, addressing the “blank page problem” for many large organizations. They also write boilerplate code for modern frameworks like Playwright and Cypress. This assistance allows future of QA automation to focus on high-level strategy rather than syntax.

The role of the manual tester is not dying; it is gentrifying into an elite skill set. We are seeing a sharp decline of manual regression testing, as 46% of teams have already replaced half or more of their manual efforts with intelligent test automation. The modern Quality Engineer acts as a strategic auditor and “AI Red Teamer,” using human cunning to trick AI systems into failure—a task no script can perform. This evolution demands deeper domain knowledge and AI literacy, as testers must now verify the probabilistic logic of LLMs.

The Efficiency Paradox: Shifting Quality Everywhere

One of the most counter-intuitive software testing predictions 2026 is the visible contraction of dedicated QA budgets. Historically, as software complexity grew, organizations funneled up to 35% of their IT spend into testing. Recent data reveals a reversal, with QA budgets dropping to approximately 26% of IT spend. This decline does not signal a deprioritization of quality; rather, it represents a “deflationary dividend” powered by intelligent test automation.

We are seeing the rise of a hybrid “Shift-Left and Shift-Right” model that embeds quality into every phase of the lifecycle. The economic logic for shifting left is irrefutable: fixing a defect during the design phase costs pennies, while fixing it post-release can cost 15 times more. By 2025, nearly all DevOps-centric organizations will have adopted shift-left practices, making developers responsible for writing unit and security tests directly within their IDEs.

Simultaneously, the industry is embracing shift-right strategies to validate software in the chaos of live production. Teams now use observability and chaos engineering to monitor real-user behavior and system resilience in real time. This constant testing loop causes a phenomenon known as “budget camouflage”.

When a developer configures a security scan in a CI/CD pipeline, the cost is often filed under “Engineering” or “Infrastructure” rather than a dedicated QA line item. The result is a leaner, more distributed future of QA automation that delivers higher reliability at a lower visible cost.

Guardians of the Model: QA’s Critical Role in AI Governance and Risk

As enterprises rush to deploy Large Language Models (LLMs) and Generative AI, a new challenge emerges: the “trust gap”. While the potential of AI is immense, nearly 73% of testers do not trust AI output alone. This skepticism stems from the probabilistic nature of LLMs, which are prone to hallucinations—generating test cases for non-existent features or writing functionally flawed code. Consequently, AI-driven test automation trends are shifting the QA focus from simple bug-hunting to robust AI governance.

Testing GenAI-based applications requires a fundamental change in methodology. Traditional deterministic testing, where a specific input always yields the same output, does not apply to LLMs. Instead, QA teams must now perform “AI Red Teaming”—deliberately trying to trick the model into producing biased, insecure, or incorrect results. This role is vital for compliance with emerging regulations like the EU AI Act, which is expected to create new, stringent testing requirements for companies deploying AI in Europe by 2026.

Modern quality engineering must also address the “Data Synthesis” challenge. Organizations are increasingly using GenAI to create synthetic test data that mimics production environments while remaining strictly compliant with privacy laws like GDPR and CCPA. This practice ensures that future of QA automation remains secure and ethical. By 2026, the primary metric for QA success will move beyond defect counts to “Risk Mitigation Efficiency,” measuring how effectively the team identifies and neutralizes the subtle logic gaps inherent in AI-driven systems.

Specialized Frontiers: Navigating 5G, IoT, and the Autonomous Horizon

The final piece of the 2026 puzzle lies in the physical world. As software expands into specialized hardware, the global 5G testing market is surging toward $8.39 billion by 2034. We are moving beyond web browsers into massive IoT ecosystems where connectivity and latency are the primary failure points. Network slicing—where operators create virtual networks optimized for specific tasks—introduces a level of complexity that traditional tools simply cannot handle.

In these high-stakes environments, such as medical IoT or autonomous vehicles, the margin for error is non-existent. While a consumer web app might tolerate three defects per thousand lines of code, critical IoT targets less than 0.1 defects per KLOC. This demand for absolute reliability is driving a massive spike in security testing, which has become the top spending priority in the IoT lifecycle. We are also seeing the explosive growth of blockchain testing, with a CAGR exceeding 50% as enterprises adopt immutable ledgers for supply chains.

Qyrus: Orchestrating the Autonomous Quality Frontier

Qyrus does not just follow AI trends in software testing; it builds the infrastructure to make them operational. As the industry moves toward agentic autonomy, Qyrus acts as the bridge. Through NOVA, our autonomous test generation engine, and Sense-Evaluate-Execute-Report (SEER), our agentic orchestration layer, we enable teams to transition from manual script-writing to goal-oriented intelligent test automation. These tools do more than suggest code; they navigate complex application logic to achieve business outcomes, fulfilling the software testing predictions 2026 that favor intent over static steps.

To solve the maintenance crisis—where “flakiness” consumes 40% of team capacity—Qyrus provides Healer AI. This self-healing technology automatically repairs brittle scripts by identifying UI changes through context and computer vision. By automating the drudgery of maintenance, Healer AI frees your engineers for high-value exploratory work.

Furthermore, Qyrus modernizes the entire stack by providing Data Testing capabilities and a unified cloud-native environment. Whether it is Web, Mobile, API, or Desktop, our platform allows developers and business users to collaborate seamlessly, making the future of QA automation a “shift-left” reality.

For specialized frontiers like BFSI and IoT, Qyrus offers enterprise-grade solutions like our Real Device Farm and dedicated SAP Testing modules. These tools are designed for high-stakes environments where reliability targets are often stricter than 0.1 defects per KLOC.

Finally, as organizations face the “trust gap” in GenAI adoption, Qyrus introduces Determinism on Demand. This ensures that while you leverage the power of probabilistic AI, your testing remains grounded in verifiable logic. Qyrus provides the governance and risk mitigation needed to turn AI-driven test automation trends into a secure, competitive advantage.

Finalizing Your Strategy: The Road to 2030

The transition from “Quality Assurance” to “Quality Engineering” is not just a change in title—it is a change in survival strategy. As we head toward 2030, the organizations that thrive will be those that treat quality as a strategic intelligence function rather than a release-day hurdle. By leveraging intelligent test automation and autonomous agents, you can bridge the “trust gap” and deliver digital experiences that are not just functional, but fundamentally trustworthy.

Looking toward, the vision is one of complete autonomy. We expect intelligent test automation to manage the entire testing lifecycle—from discovery to self-healing—without explicit human intervention. The U.S. Bureau of Labor Statistics projects a 15% growth for testers through 2034, but the roles will look very different. The successful Quality Engineer of the future will be a pilot of AI agents, focusing on strategic business value and delightful user experiences rather than manual validation.

Stop Testing the Past. Start Engineering the Future.

The leap to autonomous quality doesn’t have to be a leap into the unknown. Whether you are battling brittle scripts, scaling for 5G, or navigating the risks of GenAI, Qyrus provides the AI-native infrastructure to help you lead the shift.

Book a Demo with Qyrus Today and see how we can transform your testing lifecycle into a competitive advantage.

Qyrus, a provider of AI-powered software testing solutions to enterprises, today announced that it has been named a Leader in The Forrester Wave™: Autonomous Testing Platforms, Q4 2025. The report evaluated the 15 most significant providers in the market based on 25 criteria.

As organizations increasingly integrate artificial intelligence into their software development lifecycles, the demand for autonomous testing solutions that can validate both the applications and the AI models within them has surged. In this evaluation, Qyrus received the highest score possible (5.0) in the Roadmap, Testing AI Across Different Dimensions, Testing RAG Pipelines, Level of Autonomous Testing, Pricing Flexibility and transparency, and Testing Agentic Tool Calling criteria.

“We believe being named a Leader in a Forrester report is tremendous evidence of our vision to transform quality engineering through Agentic AI,” said Ravi Sundaram, President at Qyrus. “As enterprises move from simple automation to true autonomy, we are dedicated to providing a platform that not only accelerates release velocity but also ensures trust in the generative AI systems building our future.”

The report notes that Qyrus “excels in AI testing dimensions, using heuristics and LLM to judge faithfulness, relevance, and coverage.” With the rise of agentic workflows, Qyrus has focused heavily on agentic test orchestration. The report states, “Its Sense to Evaluate to Execute to Report (SEER) orchestration framework and excellent agentic tool calling result in an above-par score for autonomous testing”.

Qyrus’ platform enables enterprises to scale their testing efforts across web, mobile, and API layers while addressing the specific complexities of modern AI applications. In the report’s “Forrester’s Take” section, the report concludes that “Qyrus suits enterprises seeking advanced AI-driven testing, multiagent orchestration, and robust validation of genAI outputs at speed and scale”.

Qyrus believes its recognition as a Leader underscores its commitment to innovation and its ability to support customers as they navigate the complexities of testing in an AI-first world.

This News Release is originally published on EIN Presswire

Disclaimer

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here.

We stopped asking “can we automate this?” in 2025. Instead, we started asking a much harder question: “How much can the system handle on its own?”

This year changed the rules for software quality. We witnessed the industry pivot from simple script execution to genuine autonomy, where AI doesn’t just follow orders—it thinks, heals, and adapts. The numbers back this shift. The global software testing market climbed to a valuation of USD 50.6 billion , and 72% of corporate entities embraced AI-based mobile testing methodologies to escape the crushing weight of manual maintenance.

At Qyrus, we didn’t just watch these numbers climb. We spent the last twelve months building the infrastructure to support them. From launching our SEER (Sense-Evaluate-Execute-Report) orchestration framework to engaging with thousands of testers in Chicago, Houston, Santa Clara, Anaheim, London, Bengaluru, and Mumbai, our focus stayed sharp: helping teams navigate a world where real-time systems demand a smarter approach.

This post isn’t just a highlight reel. It is a report on how we listened to the market, how we answered with agentic AI, and where the industry goes next.

The Pulse of the Industry vs. The Qyrus Answer

We saw the gap between “what we need” and “what tools can do” narrow significantly this year. We aligned our roadmap directly with the friction points slowing down engineering teams, from broken scripts to the chaos of microservices.

The GenAI & Autonomous Shift

The industry moved past the novelty of generative AI. It became an operational requirement. Analysts estimate the global software testing market will reach a value of USD 50.6 billion in 2025, driven largely by intelligent systems that self-correct rather than fail. Self-healing automation became a primary focus for reducing the maintenance burden that plagues agile teams.

We responded by handing the heavy lifting to the agents.

Healer 2.0 arrived in July, fundamentally changing how our platform interacts with unstable UIs. It doesn’t just guess; it prioritizes original locators and recognizes unique attributes like data-testid to keep tests running when developers change the code.

We launched AI Genius Code Generation to eliminate the blank-page paralysis of writing custom scripts. You describe the calculation or logic, and the agent writes the Java or JavaScript for you.

Most importantly, we introduced the SEER framework (Sense, Evaluate, Execute, Report). This isn’t just a feature; it is an orchestration layer that allows agents to handle complex, multi-modal workflows without constant human hand-holding.

Democratization: Testing is Everyone’s Job

The wall between “testers” and “business owners” crumbled. With manual testing still commanding 61.47% of the market share, the need for tools that empower non-technical users to automate complex scenarios became undeniable.

We focused on removing the syntax barrier.

TestGenerator now integrates directly with Azure DevOps and Rally. It reads your user stories and bugs, then automatically builds the manual test steps and script blueprints.

We embedded AI into the Qyrus Recorder, allowing users to generate test scenarios simply by typing natural language descriptions. The system translates intent into executable actions.

The Microservices Reality Check

Monolithic applications are dying, and microservices took their place. This shift made API testing the backbone of quality assurance. As distributed systems grew, teams faced a new problem: testing performance and logic across hundreds of interconnected endpoints.

We upgraded qAPI to handle this scale.

We introduced Virtual User Balance (VUB), allowing teams to simulate up to 1,000 concurrent users for stress testing without needing expensive, external load tools.

We added AI Automap, a feature where the system analyzes your API definitions, identifies dependencies, and autonomously constructs the correct workflow order.

Feature Flashback

We didn’t just chase the AI headlines in 2025. We spent thousands of engineering hours refining the core engines that power your daily testing. From handling complex loops in web automation to streamlining API workflows, we shipped updates designed to solve the specific, gritty problems that slow teams down.

Here is a look at the high-impact capabilities we delivered across every module.

Web Testing: Smarter Looping & Debugging

Complex logic often breaks brittle automation. We fixed that by introducing Nested Loops and Loops Inside Functions, allowing you to automate intricate scenarios involving multiple related data sets without writing a single line of code.

Resilient Execution: We added a Continue on Failure option for loops. Now, a single failed iteration won’t halt your entire run, giving you a complete report for every data item.

Crystal Clear Reports: Debugging got faster with Step Descriptions on Screenshots. We now overlay the specific action (like “go to url”) directly on the execution image, so you know exactly what happened at a glance.

Instant Visibility: You no longer need to re-enter “record mode” just to check a technical detail. We made captured locator values immediately visible on the step page the moment you stop recording.

API Testing: Developer-Centric Workflows

We focused on making qAPI speak the language of developers.

Seamless Hand-offs: We expanded our code generation to include C# (HttpClient) and cURL snippets, allowing developers to drop your test logic directly into their environment.

Instant Migration: Moving from manual checks to automation is now instant. The Import via cURL feature lets you paste a raw command to create a fully configured API test in seconds.

AI Summaries: Complex workflows can be confusing. We added an AI Summary feature that generates a concise, human-readable explanation of your API workflow’s purpose and flow.

Expanded Support: We added native support for x-www-form-urlencoded bodies, ensuring you can test web form submissions just as easily as JSON payloads.

Mobile Testing: The Modular & Agentic Leap

Mobile testing has long been plagued by device fragmentation and flaky infrastructure. We overhauled the core experience to eliminate “maintenance traps” and “hung sessions.”

Uninterrupted Editing: We solved the context-switching problem. You can now edit steps, fix logic, or tweak parameters without closing the device window or losing your session state.

Modular Design: Update a “Login Block” once, and it automatically propagates to every test script that uses it. This shift from linear to component-based design reduces maintenance overhead by up to 80%.

Agentic Execution: We moved beyond simple generation to true autonomy. Our new AI Agents focus on outcomes—detecting errors, self-healing broken tests, and executing multi-step workflows without constant human prompts.

True Offline Simulation: Beyond basic throttling, we introduced True Offline Simulation for iOS and a Zero Network profile for Android. These features simulate a complete lack of internet connectivity to prove your app handles offline states gracefully.

Desktop Testing: Security & Automation

For teams automating robust desktop applications, we introduced features to harden security and streamline execution.

Password Masking: We implemented automatic masking for global variables marked as ‘password’, ensuring sensitive credentials never appear in plain text within execution reports.

Test Scheduling: We brought the power of “set it and forget it” to desktop apps. You can now schedule complex end-to-end desktop tests to run automatically, ensuring your heavy clients are validated nightly without manual intervention.

Test Orchestration: Control & Continuity

Managing end-to-end tests across different platforms used to be disjointed. We unified it.

Seamless Journeys: We introduced Session Persistence for web and mobile nodes. You can now run a test case that spans 24 hours without repeated login steps, enabling true “day-in-the-life” scenarios.

Unified Playback: Reviewing cross-platform tests is now a single experience. We generate a Unified Workflow Playback that stitches together video from both Web and Mobile services into one consolidated recording.

Total Control: Sometimes you need to pull the plug. We added a Stop Execution on Demand feature, giving you immediate control to terminate a wayward test run instantly.

Data Testing: Modern Connectivity

Data integrity is the silent killer of software quality. We expanded our reach to modern architectures.

NoSQL Support: We released a MongoDB Connector, unlocking support for semi-structured data and providing a foundation for complex nested validations.

Cloud Data: We built a direct Azure Data Lake (ADLS) Connector, allowing you to ingest and compare data residing in your Gen2 storage accounts without moving it first.

Efficient Validation: We added support for SQL LIMIT & OFFSET clauses. This lets you configure “Dry Run” setups that fetch only small data slices, speeding up your validation cycles significantly.

Analyst Recognition

Innovation requires validation. While we see the impact of our platform in our customers’ success metrics every day, independent recognition from the industry’s top analysts confirms our trajectory. This year, two major firms highlighted Qyrus’ role in defining the future of quality.

Leading the Wave in Autonomous Testing

We secured a position as a Leader in The Forrester Wave™: Autonomous Testing Platforms, Q4 2025.

This distinction matters because it evaluates execution, not just vision. We received the highest possible score (5.0) in critical criteria including Roadmap, Testing AI Across Different Dimensions, and Testing Agentic Tool Calling. The report specifically noted our orchestration capabilities, stating that our SEER framework (Sense, Evaluate, Execute, Report) and “excellent agentic tool calling result in an above-par score for autonomous testing”.

For enterprises asking if agentic AI is ready for production, this report offers a clear answer: the technology is mature, and Qyrus is driving it.

Defining GenAI’s Role in the SDLC

Earlier in the year, Gartner featured Qyrus in their report, How Generative AI Impacts the Software Delivery Life Cycle (April 2025).

As developers adopt GenAI to write code faster—reporting productivity gains of 10-15%—testing often becomes the bottleneck. Gartner identified Qyrus as an example vendor for AI-augmented testing, recognizing our ability to keep pace with these accelerated development cycles. We don’t just test the code humans write; we validate the output of the generative models themselves, ensuring that speed does not come at the cost of reliability.

Community & Connection

We didn’t spend 2025 behind a desk. We spent it in conference halls, hackathons, and boardrooms, listening to the engineers and leaders who are actually building the future. From Chicago to Bengaluru, the conversations shifted from “how do we automate?” to “how do we orchestrate?”

Empowering the SAP Community

We started our journey with the ASUG community, where the focus was squarely on modernizing the massive, complex landscapes that run global business. In Houston, Ravi Sundaram challenged the room to look at agentic SAP testing not as a future luxury, but as a current necessity for improving ROI. The conversation deepened in New England and Chicago, where we saw firsthand that teams are struggling to balance S/4HANA migration with daily execution. The consensus across these chapters was clear: SAP teams need strategies that reduce overhead while increasing confidence across integrated landscapes.

We wrapped up our 2025 event journey at SAP TechEd Bengaluru in November with two energizing days that put AI-led SAP testing front and center. As a sponsor, we brought a strong mix of thought leadership and real-world execution. Sessions from Ameet Deshpande and Amit Diwate broke down why traditional SAP automation struggles under modern complexity and demonstrated how SEER enables teams to stop testing everything and start testing smart. The booth buzzed with discussions on navigating S/4HANA customizations, serving as a powerful reminder that the future of SAP quality is intelligent, adaptive, and already taking shape.

Leading the Global Conversation

In August, we took the conversation global with an exclusive TestGuild webinar hosted by Joe Colantonio. Ameet Deshpande, our SVP of Product Engineering, tackled the industry-wide struggle of fragmentation—where AI accelerates development, but QA falls behind due to disjointed tools. This session marked the public unveiling of Qyrus SEER, our autonomous orchestration framework designed to balance the Dev–QA seesaw. The strong live attendance and post-event engagement reinforced that the market is ready for a shift toward unified, autonomous testing.

The momentum continued in September at StarWest 2025 in Anaheim, where we were right in the middle of the conversations shaping the future of software testing. Our booth became a go-to spot for QA leaders looking to understand how agentic, AI-driven testing can keep up with an increasingly non-deterministic world. A standout moment was Ameet Deshpande’s keynote, where he challenged traditional QA thinking and unpacked what “quality” really means in an AI-powered era—covering agentic pipelines, semantic validation, and AI-for-AI evaluation.

Redefining Financial Services (BFSI)

Banking doesn’t sleep, and neither can its quality assurance. At the BFSI Innovation & Technology Summit in Mumbai, Ameet Deshpande introduced our orchestration framework, SEER, to leaders facing the pressure of instant payments and digital KYC. Later in London at the QA Financial Forum, we tackled a tougher reality: non-determinism. As financial institutions embed AI deeply into their systems, rule-based testing fails. We demonstrated how multi-modal orchestration validates these adaptive systems without slowing them down, proving that “AI for AI” is already reshaping how financial products are delivered.

The Developer & API Ecosystem

APIs drive the modern web, yet they often get tested last. We challenged this at API World in Santa Clara, where we argued that API quality deserves a seat at the table. Raoul Kumar took this message to London at APIdays, showing how no-code workflows allow developers to adopt rigorous testing without the friction. In Bengaluru, we saw the scale of this challenge up close. At APIdays India, we connected with architects building for one of the world’s fastest-growing digital economies, validating that the future of APIs relies on autonomous, intelligent quality.

Inspiring the Next Generation

Innovation starts early. We closed the year as the Technology Partner for HackCBS 8.0 in New Delhi, India’s largest student-run hackathon. Surrounded by thousands of student builders, we didn’t just hand out swag. We put qAPI in their hands, showing them how to validate prototypes instantly so they could focus on creativity. Their curiosity reinforced a core belief: when you give builders the right tools, they ship better software from day one.

Conclusion: Ready for 2026

2025 was the year we stopped treating “Autonomous Testing” as a theory. We proved it is operational, scalable, and essential for survival in a market where software complexity outpaces human capacity.

We are entering 2026 with a platform that understands your code, predicts your failures, and heals itself. Whether you need to validate generative AI models, streamline a massive SAP migration, or ensure your APIs hold up under peak load, Qyrus has built the infrastructure for the AI-first world.

The tools are ready. The agents are waiting. Let’s build the future of quality together.

Book a Demo

The world of software testing moves fast, and staying ahead requires tools that not only keep pace but actively drive innovation. At Qyrus, we’re relentlessly focused on evolving our platform to empower your teams, streamline your workflows, and make achieving quality more intuitive than ever before. May was a busy month behind the scenes, packed with exciting new features and significant enhancements designed to give you even more power and flexibility in your testing journey.

Get ready to explore the latest advancements we’ve rolled out across the Qyrus platform!

Complex Web Tests, Now Powered by AI Genius!

Manual coding for complex calculations in web tests? Consider it a thing of the past! We’re thrilled to introduce a game-changing AI feature that lets you generate custom Java and JS code using simple, natural language descriptions. Just tell Qyrus what you need the code to do, and our AI gets to work, even understanding the variables you’ve already set up in your test. This AI Text-to-Code conversion is seamlessly integrated with our Execute JS, Execute JavaScript, and Execute Java actions, designed to produce accurate, executable snippets right when you need them. You maintain control, of course – easily review, modify, or copy the generated code before using it.

A quick note: This powerful AI code generation is currently a Beta feature, and we’re actively refining it based on your feedback!

Enhanced Run Visibility for Web Tests

But that’s not all for Web Testing this month. For our valued enterprise clients, managing your test runs just got clearer. You now have enhanced visibility into your test execution queues, allowing you to see detailed information, including the exact position of your test run in the queue. Gain better insight, plan more effectively, and stay informed every step of the way.

Sharper Focus for Your Mobile Visuals

Visual testing on mobile is crucial, but sometimes you need to tell your comparison tools to look past dynamic elements or irrelevant areas. This month, we’ve enhanced our Mobile Testing Mobile Testing capabilities to give you more granular control. You can now easily ignore specific areas within your mobile application screens, excluding those regions entirely from visual comparisons.

Additionally, you can ignore the header or footer of the screen meaning that you can easily compare different execution results and not run into issues due to differences in the notification bar or in a footer.

This means cleaner, more relevant results and less noise when you’re ensuring your app looks exactly as it should across devices. Focus on what truly matters for your app’s user interface integrity.

Device Farm: Smoother Streaming, Better Guidance

We know your time on the Device Farm Device Farm streaming screen is valuable, and a smooth experience is key. This month, we’ve rolled out several user experience improvements to make your interactions even more intuitive. The tour guide text has been refined to be more informative, guiding you clearly through the features.

We’ve also added a Global Navbar directly inside the device streaming page, providing consistent navigation right where you need it. Plus, for those times you’re working with a higher zoom percentage, we’ve included a handy scroll bar to make navigating the page much easier. Small changes, big impact on your workflow!

Desktop Testing: Schedule Your Success

We’re excited to announce that test scheduling is now available in Qyrus Desktop Testing. This highly requested feature, already familiar from other modules, brings a new level of automation to your desktop workflows. It’s particularly powerful for those complex end-to-end test cases that span across different modules, perhaps starting in a web portal, moving through a back office, and ending in servicing.

Now, you can schedule these crucial test flows, ensuring your regression suites run automatically, even aligning with deployment schedules. This means no more worrying about desktop availability at the exact moment of execution – Qyrus handles it for you. With this feature, efficiently managing tests for workflows impacting dozens of test cases becomes significantly simpler.

Smarter AI for Broader Test Coverage

Our commitment to leveraging AI to make testing more intelligent continues this month with key improvements to both TestGenerator and TestGenerator+. We’ve been refining these powerful features under the hood, and the result is simple but significant: you should now see more tests built by the AI compared to previous versions.

Remember, TestGenerator is designed to transform your JIRA tickets directly into actionable test scenarios, bridging the gap between development tasks and testing needs. TestGenerator+ takes it a step further, actively exploring untested areas of your application, intelligently identifying gaps, and helping you increase your overall test coverage. These enhancements mean our AI is working even harder to help you achieve comprehensive and efficient testing with less manual effort.

Ready to Experience the May Power-Ups?

This month’s Qyrus updates are all about putting more power, intelligence, and efficiency directly into your hands. From harnessing AI to generate complex web code to gaining sharper insights from mobile visual tests, scheduling your desktop workflows, and boosting the output of our AI test generators – every enhancement is designed with your success in mind. We’re dedicated to providing a platform that adapts to your needs, streamlines your processes, and helps you deliver quality software faster than ever before.

Excited to see these May power-ups in action? There’s no better way to understand the impact Qyrus can have on your testing journey than by experiencing it firsthand.

Ready to learn more or get started?

Book a Personalized Demo Today | Start Your Free Qyrus Trial

And don’t forget to explore our documentation for more details on these new features!

Explore Qyrus Documentation

We’re constantly building, innovating, and looking for ways to make your testing life easier. Stay tuned for more exciting updates from Qyrus!