Introducing the new streaming agent: Making AI decisions transparent
Artificial Intelligence is currently disrupting software testing, however, a major concern for enterprises remains trust. How can anybody be sure that AI-driven testing recommendations, automation scripts, or test optimizations are actually reliable? The answer lies in Explainable AI, a now growing necessity in the AI Space.
SQAI Suite is taking an important step in this direction by introducing an AI Agent that live-streams” its answers as it processes requests. This ensures a fair level transparency into how our AI Ecosystem derives answers, what (and when) knowledge it consults, and how it interprets specific testing contexts.
In this article, we’ll explore:
- Why Explainable AI Practices are crucial in test automation
- How SQAI Suite’s AI Agent with real-time reasoning transparency is a valuable asset
- Practical use cases where this feature makes an impact
- Why this positions SQAI Suite ahead of competitors in AI-driven software testing industry
The Need for Explainable AI in Test Automation
AI-powered test automation tools have become sophisticated, leveraging Large Language Models and advanced RAG techniques to generate test cases, automate scripts and optimize regression suites. However, most AI systems today function as black boxes… meaning users input a request, and AI outputs a result, leaving everybody guessing about the underlying logic.
Key Challenges Without Explainable AI:
- Lack of trust: Humans don’t understand how AI reaches certain conclusions.
- Difficult debugging: Whenever an AI-generated test case fails, it’s unclear where it comes from.
- Compliance risks: Enterprises dealing with GDPR, SOC2, or ISO27001 need full traceability…
- Hallucinations : AI models do still fabricate fictional information from time to time, making it essential to track knowledge sources.
For most companies, the risk of blindly adopting AI-generated test cases without understanding their origins is simply too high. That’s where SQAI Suite’s AI Agent with transparency steps in.
How SQAI Suite’s AI agent ensures explainability
SQAI Suite now has integrated real-time explainability into its AI Agents. Unlike traditional AI implementations, which provide a more static output, SQAI’s AI Agent streams its answering process live as it is working for you.
You’ll get a:
- Live Reasoning Feed, you can see in real time how SQAI Suite forms an answer and what knowledge sources it consults.
- Traceable knowledge sources, SQAI can consult your most important sources with ease, focussed on test documentation, Confluence, online resources, Xray test cases, API specifications, and code repositories to provide contextually accurate responses for example.
- Compliance by default, SQAI Suite ensures that test decisions and sources remain auditable, therefore supporting industries with strict regulatory requirements (like finance, healthcare, and automotive).
These features bridge the gap between AI automation and human testers by providing the transparency required to make AI not just powerful, but also accountable and trustworthy.
Real-World Use Cases of AI Explainability in Testing
AI-Generated Test Cases with Justification
Traditional AI Output:
- Generates test cases, but testers don’t know if it referenced the latest requirements or outdated documentation in the knowledge base.
SQAI Suite’s AI Agent Output:
- The AI streams its reasoning live, showing that it pulled test steps from Confluence, online guides and knowledge base documents, and validated API specs from Swagger docs for example
Impact: Testers have more trust in the output and can still adjust the test case if needed, reducing misalignment between human and machine.
Debugging AI-Generated Test Scripts
Traditional AI Issue:
- Little to no insights into why the the AI Agent selected certain locators or assertions.
SQAI Suite’s AI Agent Advantage:
- SQAI Suite shows the reasoning process, explaining that it:
- Chose a specific assertion method based on past documented failures in similar test cases
Impact: Testers can now debug efficiently without having to reverse-engineer the full logic, saving hours of manual effort.
Ensuring Regulatory Compliance in AI-Driven Testing
Traditional AI Issue:
- No way to prove which knowledge sources AI used to generate test automation logic.
SQAI Suite’s AI Agent Solution:
- Full traceability of knowledge sources.
- Test managers can audit AI-generated test logic end-to-end and ensure that it doesn’t include sensitive user data or personal identifiers.
Impact: SQAI Suite remains compliant, auditable, and enterprise-ready.
Why This Sets SQAI Suite Apart from Competitors
Most AI-powered testing tools offer some level of automation, but few (if any) provide true real-time explainability.
Comparison to Market Alternatives
Feature | SQAI Suite AI Agent | Traditional AI Testing Tools |
Live answering stream | ✅ Yes | ❌ No |
Traceable knowledge sources | ✅ Yes | ❌ No |
Step-by-step justification | ✅ Yes | ❌ No |
Compliance-friendly AI decision-making | ✅ Yes | ❌ No |
SQAI Suite ensures that AI is transparent, accountable, and enterprise-ready assistant for software testing.
Final Thoughts: Why explainable AI is the future of test automation
As AI-driven test orchestration becomes more advanced, enterprises will demand more than just that, they will require transparency, trust, and accountability.
With the AI Agent’s real-time reasoning, teams can now:
- Build trust in AI-generated content
- Debug test failures faster with full AI transparency
- Ensure compliance & regulatory adherence
- collaboration between AI & human testers
As Explainable AI (XAI) becomes an industry standard, SQAI Suite is already ahead of the curve, ensuring that AI is not just powerful, but accountable.
🚀 Want to see the AI Agent in action? Schedule a demo with SQAI Suite and experience real-time AI transparency today!