Agentic AI in quality management explained

Software delivery is moving faster than ever, but traditional quality management processes alone can’t keep up. According to the Tricentis quality transformation report, 40% of organizations lose over $1 million each year because of poor software quality. This shows that manual and reactive QA methods are reaching their limits.

This raises an important question: How can artificial intelligence (AI), especially agentic AI, help overcome these limits? In this post, we’ll break down what agentic quality management is, how it addresses modern QA challenges, and how Tricentis solutions are making this intelligent, automated approach a reality.

The challenges of traditional quality management

Traditional quality management has delivered enormous value over the years, but alone, it just can’t keep up with how fast things move now, and it’s not really hard to see why.

1. Fragmented and manual processes

In most organizations, quality activities (like audits and customer feedback) are scattered across silos, preventing real-time connections and leading to missed prevention opportunities.

2. Reactive quality control

Most teams, especially startups, just ship fast without any testing, and when users complain, they’re stuck wasting time fixing issues they could’ve prevented.

3. Limited data integration

Most quality teams still collect and track information manually through forms, audits, and spreadsheets, which are unfortunately slow and error-prone. By the time a trend becomes obvious in a monthly report, the damage is already done.

4. Slow continuous improvement cycles

And finally, PDCA (Plan–Do–Check–Act) and DMAIC (Define–Measure–Analyze–Improve–Control) remain valuable frameworks, but their human-dependent cycles struggle in high-velocity environments.

They rely too much on people doing every step, like collecting data, analyzing, tweaking things, and checking again. When you’ve got thousands of data points flying in every day, that kind of manual loop just can’t keep up.

With these challenges, we can see that these traditional models are holding quality back, and it’s time for something smarter to take over.

You can view agentic AI like an AI with initiative

What is agentic AI, and what makes it different?

You can view agentic AI like an AI with initiative. It doesn’t just wait for instructions or give suggestions; it thinks through what needs to be done and takes action on its own.

IBM describes it as “an artificial intelligence system that can accomplish a specific goal with limited supervision,” which basically means it’s smart enough to handle tasks without being micromanaged.

This is where it really stands apart from generative AI. Generative AI is reactive, meaning it does things when you ask. Agentic AI is proactive. It plans, reasons, makes decisions, and executes tasks from start to finish.

Now, imagine bringing that kind of autonomy into quality management, where instead of constantly checking your dashboards or running all your tests manually, you have AI agents that watch over the system nonstop, learn from live data and feedback, spot risky code changes, prioritize tests, and even trigger regression runs automatically.

Benefits of agentic AI in quality management

When applied to quality management, agentic AI has the potential to transform how teams detect risk, perform testing, and release software. Here’s how it solves modern QA challenges and delivers real, measurable impact across the entire quality life cycle:

1. Speeds up feedback loops

Agentic AI is capable of running continuously to detect anomalies and trigger follow-up actions without human supervision, using autonomous reasoning and automation.

For example, a risk-detection agent monitors every code change as it’s committed, while a regression-testing agent recalculates the risk score, launches focused tests, and blocks faculty merges if failures occur, reducing the entire test-fix-deploy process from hours or days to just minutes.

2. Improves coverage and reliability

Agentic systems use historical defect and test data to refine prediction models (flagging high-risk code, modules, or changes) and even auto-repair test scripts when needed. They improve themselves over time, increasing test coverage and reducing blind spots.

3. Supports continuous delivery

In high-velocity environments like DevOps, agentic systems enable quality processes to scale and adapt at delivery speed, removing manual slowdowns.

An example of this is a deployment pipeline that includes an agent monitoring every stage—build, test, deploy—and adapting its thresholds (like acceptable error rates, test pass percentages, code coverage levels) dynamically. Because of that, teams can release more frequently but with fewer post-deployment defects.

4. Breaks data silos

Agentic systems integrate multiple data sources (build logs, defect trackers, test results). With this unified data, the system can detect patterns across domains (like a build change, test failure, or defect spike) instead of reacting only within one silo. Then it can flag modules with issues, helping the quality team see the full picture early.

Although these benefits show how far agentic AI has come, the technology is still evolving. Most current solutions are built to enhance and augment QA processes and teams by handling repetitive, data-heavy tasks, setting the stage for more advanced adoption.

Best practices for adopting agentic AI

Planning to integrate an agentic system? You need to set it up right, so it can learn and deliver value. Here are a few tips to keep in mind:

Agentic AI needs a clear, connected view of the whole quality process, from code commits to deployments, to actually make smart, independent calls

1. Start with visibility

Agentic AI needs a clear, connected view of the whole quality process, from code commits to deployments, to actually make smart, independent calls.

When your tools are disjointed (no unified data), the AI’s pretty much guessing, or more correctly, hallucinating. It can’t see where things went wrong or how one issue affects the next build.

2. Feed clean, consistent data to your AI models

Poorly labeled or inconsistent test results can mislead AI-driven insights, as AI is only as smart as the data you give it (garbage in, garbage out).

So it’s important to keep things consistent. Use the same naming style for test cases, stick to one way of labeling bugs, and normalize data across tools. It’ll help your AI agent see patterns and predict with more accuracy.

3. Start small and scale

Take your time to observe how the agent reacts to live build/test data before giving it broader control. You can start with a pilot project as it minimizes risk and surfaces integration issues early.

For example, you can choose one microservice with frequent releases, then let an agentic AI monitor its pipeline, predict regressions, and recommend test prioritization. Once everything is stable, you can now extend it across the entire product suite.

4. Keep humans in the loop

Continuous feedback loops, where humans review AI decisions, improve model accuracy. So, ensure your team is always in the loop to review, override, and enhance the agent’s behavior. The best outcomes come when AI agents are treated as an intelligent collaborator instead of a full replacement for human judgment.

5. Measure your improvement

Track metrics before and after adoption, like cycle time (how long it takes from work starting to finishing). Then check if the improvement shows whether the AI’s autonomous actions actually help. Agentic AI’s success isn’t about “using AI” but about measurable business outcomes.

Bringing agentic quality management to life with Tricentis

Software teams can no longer depend on a slow, disconnected quality process in a modern DevOps environment, where testing and deployments occur every day or perhaps several times a day. That’s where Tricentis steps in. Tricentis SeaLights (a quality intelligence platform) acts as the bridge to what you might call “agentic QA” (a quality system that suggests and also acts).

Data-driven insight is the backbone here. It integrates with every build, CI, and testing framework (like Jenkins and GitHub Actions), collecting code changes, test executions, build logs, production data, and many other kinds of data as well.

With all this data unified, teams can spot risk areas immediately. For example, a code commit is pushed, SeaLights maps exactly which method changed, and it flags which one is untested, ensuring you don’t push code to production without testing.

Then there’s AI-powered optimization with support for every test type (unit, integration, UI, manual). So rather than running every single test after each change (which is slow and expensive), SeaLights allows you to focus your testing where it matters most, leading to shorter testing cycles and reducing testing costs.

You also have continuous visibility with SeaLights, and you get live dashboards, trend reports, and quality gates. These tools show you “release readiness” at a glance, and you can see coverage trends, test gaps, and untested changes. With this information, you support real-time decision-making, and QA teams are no longer stuck waiting for end-of-sprint reports. As a result, this increases teams’ productivity and efficiency.

Traditional quality assurance alone cannot meet the speed and complexity of modern software development

Conclusion

Traditional quality assurance alone cannot meet the speed and complexity of modern software development. That’s where agentic AI comes in, offering a smarter way forward with automation, reasoning, and real-time decision-making.

Tricentis brings this approach to life with its quality intelligence platform, SeaLights, and it transforms raw data into clear insights. These insights lead to faster releases, and they ensure more reliable outcomes for every team.

Book a demo and discover how Tricentis SeaLights helps teams solve modern quality management challenges.

This post was written by Inimfon Willie. Inimfon is a computer scientist with skills in JavaScript, Node.js, Dart, Flutter, and Go Language. He is very interested in writing technical documents, especially those centered on general computer science concepts, Flutter, and backend technologies, where he can use his strong communication skills and ability to explain complex technical ideas in an understandable and concise manner.