Pradeep Govindasamy is the Co-Founder, President and CEO of QualiZeal.
Software testing is being fundamentally rewritten by autonomous AI agents. We’re empowering systems to learn, adapt and make intelligent decisions with minimal human instruction. And as we redefine what quality means across the IT life cycle, I believe the future lies in a framework we call HiQE: Human in Quality Engineering.
HiQE is our vision for a new model where human oversight coexists with autonomous AI agents. Human oversight becomes more strategic, while AI agents take on the repetitive, data-heavy work, such as prompt engineering, test orchestration, adaptation and risk assessment. It’s a model that’s working for us, and it can work for you, too.
If you’re thinking about bringing agentic AI into your QA practice, here’s what we’ve learned and what you can apply today.
Move From Scripts To Goal-Driven Autonomy
One of the most important mindset shifts is moving from task-based scripting to goal-driven autonomy. Traditional QA relies on detailed scripts (click A, input B, expect C) that must be rewritten every time the interface or flow changes. Agentic AI works differently. You define the objective; for example, “ensure the checkout flow handles all edge cases,” and the system figures out how to achieve it. It’s able to explore dynamic paths, build test strategies and update them as your product evolves.
In our experience, starting with high-level goals for core business flows yields the most value. These are the areas where change is frequent and where human-written scripts tend to lag. Our AI agents now generate their own test plans by drawing from a library of requirements, domain-specific scenarios and historical defects, which is something no manual process can replicate at the same scale or speed.
Let Context Drive Precision
For agentic AI to be most effective, it needs more than goals; it needs context. Powerful agents understand the history and behavior of the systems they’re testing. That means pulling in telemetry, usage data, change logs and past bug reports to shape their decisions. We use Retrieval-Augmented Generation (RAG) and Model Customization Platforms (MCP) to integrate this data into the testing process.
The result is a system that reasons rather than reacts. For instance, if the system learns that 5-second response times are typical during peak hours, it won’t raise false alarms. But it will detect and flag a 10-second performance spike as a potential issue. The more real-world data you feed into your agentic QA pipeline, the smarter and more accurate your testing becomes.
Start Small And Prove It
Agentic AI shouldn’t be rolled out across your entire QA organization all at once. The most successful implementations start in focused, high-impact areas where change is frequent and traditional automation struggles to keep up.
For us, that proof point came with a major bank’s mobile app. Weekly updates constantly broke traditional RPA scripts. New feature coverage lagged behind development by weeks. When we introduced agentic AI to run in parallel, the results were dramatic. Test maintenance dropped from 40 hours to 4 per week, new feature validation was completed within 24 hours and critical defect detection increased by 250%. Testing the system in a limited scope gave our client and their teams confidence to scale. It also gave the agents a rich learning environment to refine their capabilities.
Plan For Regulation And Legacy Systems
Of course, not every organization operates in a greenfield environment. Regulated industries like healthcare, banking and pharmaceuticals require documentation, traceability and validation at every step. Many also depend on legacy systems that still run on mainframes or are supported by poorly structured APIs and scattered documentation. These environments require more upfront planning, but agentic AI can still deliver real value.
We typically start with a dual-track validation model by running traditional QA alongside agentic QA to benchmark performance and reliability. From there, we build abstraction layers and guardrails to ensure the agent operates safely within the defined scope. And we train agents to learn from unstructured inputs like documents, videos and even voice files.
While adoption in regulated industries can take six to 12 months longer, the long-term gains in efficiency, accuracy and coverage make it a worthwhile investment.
Build Trust With Transparency
Trust is critical when AI agents start making autonomous decisions that influence product quality. That trust comes from transparency and accountability. One way of accomplishing this is through the implementation of a trust score framework to help teams decide when an agent-generated result is production-ready. These scores are tied to measurable outcomes and include thresholds that trigger manual reviews.
For example, one client required human approval for any test with less than 85% agent confidence. Six months later, they lowered that threshold to 70% after seeing consistent results, cutting manual effort by 60%. In addition to scoring, we use dashboards, decision logs and audit trails to help QA teams see exactly how and why agents make decisions. This creates a human-AI partnership where the system earns trust over time through clarity and performance.
Think In Terms Of Framework
Successfully implementing agentic AI requires building a complete pipeline that includes the right inputs (telemetry, test history, user behavior), decision logic (goals, risk assessments) and governance (review criteria, fail/pass thresholds). To support this, we created a structured evaluation framework that helps us assess agent-generated outcomes for trustworthiness, factual accuracy, hallucination detection and prompt injection vulnerabilities. It also defines entry and exit criteria for each testing phase. You can build your own version of this or adapt an open framework, but the point is this: Autonomy without structure won’t scale. You need an operational layer that allows your AI to evaluate, learn and improve in every release cycle.
Your QA Playbook For The AI Era
Agentic AI makes testing faster and smarter. It moves QA from a reactive, script-driven process to an intelligent, adaptive discipline. But like any transformation, it works best when approached deliberately.
Start with one problem. Define the goal clearly. Train your agents with context. Add the right guardrails. Let them prove their value. Then scale what works. That’s the approach we’ve taken, and it’s one that other organizations can adopt, too.
The future of quality assurance is intelligent, human-guided and built for change. The sooner we embrace it, the more resilient and innovative our software and our teams can become.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
