The shift from automation to autonomy is happening quickly, and the frameworks you choose today will define how your systems think, adapt, and scale tomorrow.
With Gartner forecasting that agentic AI will be embedded in 40% of applications by 2026, up from under 5% in 2023, it is clear that agentic intelligence is moving from experimental to essential. The question isn’t if you need to use agentic AI but how soon and how effectively you can deploy it in your systems.
The pace of the software development cycle today is relentless. In agile-driven organizations, releases happen every now and then, sometimes even multiple times a day.
For years, we have relied on traditional testing: painstakingly written, updated, and maintained to ensure quality. But now, AI-powered testing agents have come to the rescue, and they are changing the game completely.
Ever launched a product thinking everything was on point, only to have a bug slipping in that no one saw coming? You’re not alone.
In fact, according to a recent research by Gitnux, 70% of bugs are reported post-release, highlighting limited test coverage.
Now, in 2025, the testing landscape has undergone major changes. Alongside tried and tested methods like manual testing, regression testing, and automated scripts, we’re seeing the rise of something new: AI agents.
Autonomous agents are no longer a research concept; they are being deployed to handle complex, multi-step workflows across tools, departments, and even entire business units. From automating software engineering tasks to accelerating product research or managing multi-channel operations, agentic AI frameworks are powering a new class of systems that can reason, plan, and take actions with minimal human intervention.
Even after crossing the beta testing phase, QA teams find themselves stuck in long testing cycles, putting in tens of hours of manual effort before the software or a feature is ready for release. At this point, quality assurance becomes more than a checkpoint; it is the deciding factor between a successful launch that entices users and one that is forgotten.
According to a report by Gitnux, Enterprises using AI for testing have reduced their test script maintenance efforts by 65%, resulting in faster releases and more dependable software.
There are some key DevOps practices that enable organizations to automate and streamline the software development processes. Continuous Integration and Delivery (CI/CD), Continuous Deployment, Microservices, Infrastructure as Code (IaC), Containerization, Monitoring & Logging are a few of them.
Load testing is a subset of performance testing that checks a system for its availability and response time during a concurrent load of users. It ensures that an application can handle the load without compromising performance.
With the rise of digital platforms and the increasing demand for online services, companies are now under pressure to deliver high-quality software products at lightning speed. However, traditional approaches are no longer enough to ensure the desired level of quality. This is where TestOps swoops in as the key solution to address this challenge.
In an SDLC, there are several testing frameworks that help to assess if the application is progressing in the right direction. It could be in terms of usability, security, compatibility, performance, integrations, etc.
Organizations whose bottom line depends on quality software development need a robust methodology in place to assure bug-free product delivery for their clients. They must integrate a means to monitor the productivity and quality of software testing with a standard set of appropriate software testing metrics.