Wolters Kluwer drives consistent software quality at scale with agentic AI
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TL;DR
- Agentic automation uses AI agents that observe or sense, reason, and then act on their own.
- It’s a big step up from RPA (too rigid) and basic AI automation (too passive).
- It handles multi-step, unpredictable tasks and gets smarter from every outcome.
- Its top use cases are customer service, finance, HR, and software testing.
- There are still risks like bad data, unpredictable behavior, and security gaps, which is why human oversight is still needed.
- The best way to start your implementation is to pick a workflow, run a pilot, measure results, and then scale.
Discover what agentic automation is, how it works, its benefits, and how it’s shaping the future of AI-driven software testing.
Every automation system has a breaking point. It could be a UI change, an unexpected input or response format, or the task that needs to be handled needs a different tool than the one it was built around.
Traditional automation was never designed for these unexpected changes, but agentic automation is. It doesn’t wait for instructions. It reads context, makes decisions, and can adapt.
According to a recent survey, most organizations are already adopting it. In fact, “Of those adopting AI agents, two-thirds (66%) say that they’re delivering measurable value through increased productivity.” The gap between teams that understand it and teams that don’t is widening fast.
This guide covers everything you need to know about agentic automation to get up to speed.
Agentic automation refers to automation powered by AI agents that can make decisions and take actions autonomously.
What is agentic automation?
Agentic automation is the system that connects intelligent agents to real, working automation. IBM defines it clearly: “Agentic automation refers to automation powered by AI agents that can make decisions and take actions autonomously.”
It’s the application layer where multiple AI agents collaborate, integrate with enterprise tools, and automate real-world processes.
Think of an IT operations center. An agentic AI system can coordinate multiple AI agents to spot an outage, run diagnostics, try standard fixes, and alert engineers only when necessary.
Different agents could be used to handle each step, but the system coordinates them into one workflow that adjusts based on what’s happening.
Unlike traditional automation, which follows rule-based logic in scripts, agentic automation can understand context, plan its approach, and act on its own, or ask for human input. It links smart decisions across different systems, building an automation cycle that learns and improves with use.
The evolution of automation: from RPA to agentic AI
Automation did not start with AI. It started with rules.
Early businesses used robotic process automation (RPA) for repetitive work like copying data or processing invoices. It is fast and accurate. No doubt about that.
But its bottleneck is that it’s rigid. Extremely rigid. Even a small UI adjustment, like changing text on a button, could break everything. Then someone had to step in and fix it.
Then came AI-powered automation with the capability to read and summarize emails or documents and handle inputs that were not perfectly structured. Yet, it was still very reactive (it waited to be told what to do).
Agentic automation is different.
Think back to the IT operations center example from the previous section.
The agentic system didn’t have to be told there was an outage. It spotted the problem, ran diagnostics, tried standard fixes, and only looped in engineers when it had to. (That last part is what we’ll keep coming back to.)
This shift, from executing instructions to pursuing outcomes, is what makes agentic automation different from everything before it.
Now, the right reaction here is not to hand it the keys to everything and walk away.
To get real value from agentic systems, you need to treat agent outputs like a junior analyst’s first draft: useful, directionally right, but worth a second look before it goes anywhere. We’ll look at these best practices in a later section.
Difference between agentic automation, AI-powered automation, and RPA
On the surface, RPA, AI-powered automation, and agentic automation may look similar because they all automate work. They’re not. Here is a summary of their differences.
| Criteria | RPA | AI-powered automation | Agentic automation |
| Decision-making | It follows strict rules, with no deviation. | Its decisions are model-assisted based on patterns. | It uses goal-driven reasoning that can choose steps. |
| How it thinks | It executes exact instructions step by step. | It estimates the best possible output from data. | It figures out how to achieve an outcome. |
| Handles unstructured data | No. It breaks easily outside the structure. | Yes. | Yes. It handles unstructured data and can combine multiple sources dynamically. |
| Adapts to change | It fails when the workflow changes. | It can handle some variation. | It can replan and readjust mid-task. |
| Self-planning | No. | No. | Yes, it breaks goals into steps. |
| Human oversight | Very high. | Medium. Humans need to define workflows. | Lower. Needs oversight for execution and for critical decisions. |
| Best for | Repetitive tasks like data entry. | Document processing and support ticket classification. | Multi-step goals like incident resolution or IT ops triage. |
| Failure mode | It stops working when it encounters changes that weren’t captured. | It might produce subtly incorrect results. | It can take unexpected paths if not well governed. |
Now that we’ve looked at how automation has evolved and how RPA, AI-powered automation, and agentic systems differ, there’s still one thing that sits underneath all of it: the AI agent itself.
What are AI agents?
AI agents are autonomous software programs (although humans remain in the loop for oversight, feedback, and goal-setting). As Google Cloud puts it: “AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning… learn, and adapt.”
In simpler terms, you tell it what needs to be done, and it figures out how. It senses the state of its world (APIs, databases, interfaces), reasons about options, executes steps, and adapts as things change.
Imagine you assign an AI tester to check a feature. A regular AI follows a test checklist step-by-step.
An AI agent gets a goal like “ensure checkout works reliably” and figures out how: It performs actions like generating test cases, adjusting based on failures, and prioritizing tests using learned risk patterns.
So AI agents are the building blocks of agentic automation. They give automation intelligence and direction.
Rather than following set scripts, these agents take charge in goal-focused systems. They figure out what to test next, how to adjust when problems arise, and when to call for human input.
How does agentic automation work?
Agentic automation relies on its ability to combine several technologies to execute tasks that would’ve otherwise required human intervention. Here’s how it works:
1. Perception
It starts by gathering data from logs, APIs, sensors, or even user actions. That way, it stays aware of what’s happening in real time.
If a web app slows down, it could flag it and also analyze patterns, maybe noticing that a memory spike always precedes the crash.
2. Reasoning
Then it connects the dots by analyzing relationships in the data using natural language processing (NLP) or other AI capabilities. If a web app slows down, it could flag it and also analyze patterns, maybe noticing that a memory spike always precedes the crash.
3. Goal setting & decision-making
From reasoning, it plans the best move to achieve the predefined goal using, maybe, a decision tree algorithm or another advanced algorithm.
Then it moves on to making a decision, such as whether to restart the service, alert an engineer, or roll back a deployment. It weighs those options based on what’s most effective.
4. Execution
Once it decides, it acts automatically. Tasks run, alerts go out, systems stabilize.
5. Learning
This is what makes it agentic: It evaluates outcomes, gathers feedback, and uses reinforcement learning or self-supervised learning to refine its approach over time.
Agentic automation does the job, learns from outcomes, and improves over time.
Benefits of agentic automation
By combining multiple AI agents that sense, reason, and learn, agentic automation helps businesses handle complex, ever-changing environments with less friction and more foresight.
Let’s go through its benefits.
Unlike rigid traditional automation tools, agentic AI systems thrive on change.
1. Manages dynamic tasks
Unlike rigid traditional automation tools, agentic AI systems thrive on change. They adjust in real time when systems update or data shifts, and they don’t need constant reprogramming.
2. Boosts efficiency
The agent learns from every outcome, spots unnecessary steps, and automates them. Over time, it gets faster, freeing up engineers to focus on real innovation instead of routine upkeep.
3. Strengthens decision-making
By analysing live data, agentic AI systems can recommend and act, helping businesses stay proactive.
4. Elevates user experience
Natural language understanding means interactions are human-like. For instance, imagine if someone contacts your customer service and says their shipment hasn’t arrived.
A traditional system forces them through menus. An agentic AI system understands context, pulls the customer’s history, sees what happened, and converses with them.
5. Improves accuracy
Cross-checking data across multiple systems is tedious for humans, and we might miss things during manual review. Agents excel at this.
By scanning data across systems in real time, agents spot many errors (like duplicate transactions or mismatched records) before they cause costly issues.
Common applications of agentic automation
Here’s where agentic automation is making the biggest impact.
IT operations
In IT operations, it monitors all the infrastructure to detect anomalies and respond to incidents all the time. If something breaks, it does not wait. It finds the issue, tries to fix it, escalates if needed, and logs everything.
Customer service
In customer service, it goes beyond answering simple questions. It checks order history, processes refunds, escalates tricky issues, and even does follow-up and reminders. It can help business customers get help anytime.
Finance and compliance
In finance and compliance, it keeps an eye on transactions to flag anything unusual, generates reports, and checks documents against regulations.
HR and onboarding
It can send offers, collect documents, set up access, and schedule orientations. This makes new hires start smoothly and reduces HR’s paperwork.
Software testing
In software testing, you can write tests, run them, understand results, and catch issues as your codebase changes. While traditional automation breaks when there are changes, agentic automation adapts.
All of these agentic examples have commonalities: They involve multiple steps, changing decisions along the way, and don’t wait for human instruction at every step.
That’s why its application in software testing stands out. Because, unlike most use cases, this one directly shapes what gets shipped.
Agentic AI systems can find patterns from past failures and user feedback, and they can predict where an issue will happen next, shifting testing from catching bugs to preventing them.
How agentic automation is shaping the future of software testing
QA is changing. Traditionally, teams waited for code to be written, ran tests, and found what broke. But they were always behind. Agentic AI systems change that.
They can find patterns from past failures and user feedback, and they can predict where an issue will happen next, shifting testing from catching bugs to preventing them.
We’re seeing this shift in tools with capabilities like:
1. Self-healing tests
When UI elements or workflow changes occur, the system can update the test automatically instead of failing.
2. Generating test cases autonomously
Agents create or refine tests based on user flows or new code commits, ensuring broader coverage without human micromanagement.
3. Prioritizing risks adaptively
The agent analyzes which failures are more critical. It checks which ones break core business logic or customer trust and tests those ones obsessively, helping teams concentrate on the most critical cases.
Challenges of agentic automation
Despite the promise, agentic automation faces some hurdles.
1. Incorrect or fragmented data
Many agent systems fail because their inputs are unreliable. Agents hallucinate or misjudge things because of this.
2. Unpredictable behavior & reliability
Sometimes an agentic AI system picks a surprising path because its internal reasoning isn’t fully transparent or consistent. It might behave differently when inputs change slightly or drift from its expected routine without warning.
3. Expanded security risk surface
As agents connect to APIs, databases, and services, one weak endpoint (whether in the agent itself or a connected system) becomes the entry point where attackers can exploit it and move laterally through everything the agent can access.
4. Governance & ethical oversight
Agents acting on their own create accountability gaps, which raises the question: How do you audit decisions? And what about bias or misuse of personal data?
Security is probably one of the biggest things holding companies back from fully trusting agentic automation.
How is security built into agentic automation?
Security is probably one of the biggest things holding companies back from fully trusting agentic automation. These agents connect to APIs, databases, internal tools, and a lot more, so one weak spot or bad decision can spread if the system has too much freedom or access.
A lot of the risk comes from weak controls around the agents, not just the agents themselves. The main thing is keeping them limited and easy to track, too. So if something goes wrong, engineers can still step in.
Here are some of the common ways teams handle this.
1. Least-privilege access
Agents should only access what a specific task requires. A billing agent has no business touching HR records.
This matters because there could be expanded attacks. Although least-privileged access does not completely eliminate security risk, it contains the blast radius if there’s an attack.
2. Audit trails and explainability
Every action an agent takes should be logged with enough context to reconstruct the decision. This closes the accountability gap raised in governance concerns. Without it, reviewing agent behavior after an incident is almost impossible.
3. Sandboxing
Agents can be tested in isolated environments before they are deployed live. The logic here is simple: a reasoning error in a sandbox is a useful signal. The same error in production becomes an incident.
4. Human-in-the-loop checkpoints
Moving funds, changing access permissions, sending external communications, and other critical decisions should all be reviewed by a human before anything executes.
This might feel like rebuilding the exact bottleneck that automation was supposed to kill. But giving full autonomy to a system that can make a disastrous mistake in production is a riskier trade-off.
Are you ready for agentic automation?
Before implementing agentic automation, you need to know whether you’re ready.
Readiness is not a single yes or no. A team can be agent-ready in customer service and nowhere close in HR. So your assessment should be at the workflow level, instead of the organizational level. These questions will help you figure out where you stand.
1. Do you have one clearly defined workflow to start with?
Do you have one repeatable workflow with clear steps and measurable outcomes, like refund processing, to start with? If humans already struggle to explain the process clearly, your agent will likely not be efficient either.
2. Is your data reliable?
Agents do not compensate for bad inputs. If your records are outdated, your agent’s response will be incorrect while sounding confident that everything is okay.
3. Are expectations realistic?
Misaligned expectations between all stakeholders most of the time cause more implementation failures than bad tooling. So, conversations around expectations should be done early so everyone is on the same page.
4. Do you have a feedback loop in place?
It’s tempting to jump from pilot to full rollout. But there’s an MIT report saying 95% AI pilots don’t really lead to impact—meaning the default outcome of an unmonitored project is failure. So your metrics and feedback will help you know if it’s something you should scale.
5. Do you have governance?
When an agent approves the wrong payment, leaks data, or sends a bad customer response, somebody needs to own that decision.
Governance means having rules, limits, and accountability around the system. Without it, teams usually panic once something breaks in production.
While modern tools have made significant progress in addressing implementation challenges, successful adoption requires careful planning and realistic expectations.
Getting started with agentic test automation
Considering agentic test automation? Start thoughtfully. While modern tools have made significant progress in addressing implementation challenges, successful adoption requires careful planning and realistic expectations.
The best approach is to start small and start smart with pilot projects. Try it in your testing or DevOps environments, where repetitive and error-prone tasks can eat up hours every week.
Here’s how to begin:
1. Pick the right playground
Start by focusing on a single workflow (like regression testing or deployment monitoring), where automation can prove quick wins.
2. Choose intelligent tools
You want platforms with AI reasoning and self-healing capabilities, not just rule-based bots. Go for systems that can detect issues, learn from fixes and outcomes, and adjust.
3. Use Tricentis’s AI-powered suite
Tosca is an agentic automation tool that helps teams generate comprehensive test cases autonomously with just natural language prompts.
LiveCompare Risk AI prioritizes high-risk areas in your SAP projects, cutting test scope by up to 85 percent using AI-powered analytics and insights.
These tools use AI to adapt to application changes and reduce maintenance, so your testing gets smarter and self-optimizes, accelerating releases in a way that’s secure, useful, and centered on achieving business goals.
Start with a specific workflow. Run it for a sprint or two and measure what changes, and watch how it all clicks.
Tricentis and agentic test automation
AI is helping teams generate and ship code faster than ever, but testing has not kept up. Traditional testing is still slow, fragile, and also hard to maintain, so a lot of software reaches production without being fully tested.
The Tricentis 2025 Quality Transformation Report says 63% of teams ship undertested code, with software defects costing many companies over $500,000 yearly. That’s the problem Tricentis is targeting with its agentic automation platforms.
Instead of relying heavily on manual testing workflows, the idea is to use intelligent agents to automate testing, adapt to changes faster, shorten release cycles, and reduce the amount of broken software reaching production.
With Tricentis Tosca’s agentic test automation, you can generate complete end-to-end tests from natural language prompts, reducing manual effort by up to 85% and boosting productivity by 60%. Learn more about agentic test automation here.
With Tricentis SeaLights AI-powered quality intelligence, you can run only the tests affected by recent code changes, so instead of burning hours on a full test suite for a minor update, your teams get more accurate test targeting and reduce manual overhead.
See more Tricentis offerings across different industries here.
How Wolters Kluwer achieved 100% test automation with Tricentis
Wolters Kluwer runs quality engineering across hundreds of applications and multiple enterprise platforms. To improve speed, consistency, and visibility across testing operations, the company adopted Tricentis solutions, including Tricentis qTest, Agentic Test Creation, and Tricentis AI Workspace.
Problem
With AI accelerating code output across the org, their existing testing approach couldn’t keep pace. Test management was scattered, test case designs were done manually, automation was inconsistent, and release decisions relied on gut feeling rather than data.
Solution
They adopted qTest to centralize test management across 500+ users, rolled out Agentic Test Creation to automate test case design directly from requirements, and implemented AI Workspace to orchestrate work across all of the agents across the org.
Outcome
- 30% faster test case design with Agentic Test Creation.
- 100% UI and API test automation on core platform applications.
- 500+ users onboarded to a single, centralized test management system.
- Better visibility into testing metrics, making go/no-go release decisions more data-driven and reliable.
To learn more about Wolters Kluwer’s implementation and results with Tricentis, visit Tricentis customer story here.
The future is agentic
Agentic automation represents a significant evolution in how systems handle complex tasks. Systems are making decisions and adapting with less manual intervention.
Tricentis is doing this at scale with tools that let teams build more resilient testing pipelines. With proper configuration and feedback loops, your automation can become more effective over time.
The future of automation is agentic and intelligent. Ready to see it in action? Explore what’s next with Tricentis AI-powered solutions.
Date: Jun. 02, 2026
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Date: Jun. 02, 2026