What is agentic automation? A beginner's guide

Traditional automation follows rule-based logic. Agentic automation evaluates context, learns from outcomes and feedback, and gets smarter over time.

Here’s how this new wave of intelligent automation is transforming workflows.

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, especially when powered by advanced AI agents.

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.

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 to making a decision such as: Should it 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. That’s the real shift: Automation that doesn’t just do the job—it gets smarter 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.

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.

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.

Strengthens decision-making

By analysing live data, agentic AI systems can recommend and act, helping businesses stay proactive.

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 their history, sees what happened, and converses with them. It knows this is their third order, remembers they’re impatient, and gets them answers fast.

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.

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:

• Self-healing tests. When UI elements or workflow changes occur, the system can update the test automatically instead of failing.
• Generating test cases autonomously. Agents create or refine tests based on user flows or new code commits, ensuring broader coverage without human micromanagement.
• 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.

Incorrect or fragmented data

Many agent systems fail because their inputs are unreliable. Agents hallucinate or misjudge things because of this.

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.

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.

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?

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 and Testim are agentic automation tools that help 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.

Conclusion

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.

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.