What Is Agentic Automation, and Why Does It Matter?

In most organizations, a business analyst may spend hours collecting data from multiple sources, cleaning it, and compiling a weekly performance report, all by hand. But what if instead of doing all this manually, the analyst could instruct a digital assistant to handle everything automatically? When instructed, the digital agent could access the necessary systems, perform the analysis, create the report, and provide useful insights.

This scenario is a practical application of agentic automation, where AI agents have the autonomy to make decisions, adapt to new situations, and act to achieve a specific goal.  

Unlike traditional automation, which follows fixed rules and human direction, agentic process automation is a dynamic solution that autonomously carries out real-life workflows.

In this guide, we’ll walk you through what agentic automation means and how it works. We’ll also explore the use cases, the possibilities it creates, and how you can start building and using it with Domo AI.

Why agentic automation matters

The modern business environment changes rapidly, generating an increasing amount of data, with customers demanding more. The market can shift instantly. Legacy automation tools, like robotic process automation (RPA), work well for repetitive tasks but often fail when things change unexpectedly due to a lack of reasoning ability.

Agentic automation addresses the current limitations and evolving needs of these older systems. The backbone of agentic automation is an AI agent, a software entity that can autonomously perceive its digital environment, such as monitoring data streams. It then reasons through this information to make decisions and act to achieve a specific goal.

For example, an AI agent can monitor a complex supply chain, identify potential problems before they occur, determine the best course of action, and execute a series of steps to resolve the issue.

Also, AI agent automation increases employee productivity by taking over routine tasks and letting them focus on strategic work. It enables businesses to quickly adapt to market changes without people overseeing everything, as AI agents make immediate decisions.

The benefits of agentic automation

Agentic process automation offers measurable benefits beyond the traditional efficiency gains, like time savings. Agents make data-based decisions, which enable a more intelligent and proactive operational model. We can expect the following benefits with agentic automation:

• End-to-end task automation: AI agents manage complete workflows on their own, from analyzing data and making decisions to carrying out tasks without delays or manual input. For example, integrating automation agents for expense processing can cut processing time by over 80 percent, lower exception rates, and improve compliance. ‍
• Proactive problem-solving: Agentic systems can monitor workflows in real time and adjust them based on new inputs and evolving conditions. Anomalies and inefficiencies are detected and addressed before they escalate. ‍
• Intelligent resource allocation: Workloads are smartly distributed based on urgency and capacity. It maximizes efficiency while preventing bottlenecks and unnecessary operational costs.  ‍
• Enhanced scalability and productivity: Agentic automation allows businesses to scale operations without a corresponding increase in headcount. One team can oversee many digital agents that work around the clock, managing fluctuating workloads and new tasks automatically, without manual reconfiguration. Also, using agentic systems, some organizations see up to 18 percent gains in employee productivity. ‍
• Personalized experiences: AI agents deliver personalized responses to users, automate requests, and provide recommendations based on their behavior and preferences. This enhances engagement and satisfaction. ‍
• Security and compliance: Sensitive data is kept safe, with automated rules to follow compliance policies, access controls, and audit trails to reduce risk and ensure regulatory adherence.

Understanding agentic process automation  

Agentic process automation combines various technologies with AI agents to execute tasks. They’re programmed with a goal but are free to choose how to achieve it based on real-time information.  

Key characteristics of agentic automation:

• Autonomy: Agents operate independently within a set of predefined boundaries and ethics. They can decide the best way to achieve goals on their own. This includes choosing which tasks to perform, in what order, and how to respond to unforeseen problems. ‍
• Adaptivity: These systems can respond to changing information, business demands, and environmental conditions. For example, if a major supplier stops working or customer demand suddenly increases, the system can automatically adjust the use of resources, shift priorities, and adapt its processes without the need to be reprogrammed. ‍
• Continuous learning: Agentic systems use feedback loops (reinforcement learning) to get smarter over time (self-learning). They learn which strategies work best under which conditions by analyzing the outcomes of their decisions.

Let’s put agentic automation in a head-to-head comparison with traditional automation to see their difference in detail. While both simplify processes and improve efficiency, their capabilities diverge greatly.

The role of AI in agentic automation

The transition from rigid, rules-based automation to digital agents is a result of breakthroughs in artificial intelligence. Early automation mimics human interactions with systems, such as logging into applications, copying data, and processing transactions.  

But, agentic automation integrates technologies like ML, NLP, and computer vision into business processes to let the agent learn, reason, and act with a degree of cognitive ability.

Here’s a closer look at technologies that power agentic automation, including:

• Machine learning (ML): This is the foundation for adaptability and continuous learning. ML algorithms enable agents to analyze data sets, identify patterns, forecast future outcomes, and refine their strategies based on performance feedback. Machine learning allows agents to improve over time at tasks such as fraud detection or optimizing logistics routes. ‍
• Natural language processing (NLP): NLP gives agents the ability to understand, interpret, and generate human language. An NLP-enabled agent can read a customer’s complaint, understand its sentiment and intent, and formulate a relevant and empathetic response. ‍
• Generative AI: Large language models (LLMs) provide agents with the capacity to create novel content and solutions. For instance, a marketing agent could use LLM to craft personalized email campaigns, while an engineering agent could write code to address a newly discovered vulnerability. The creative potential of generative AI transforms agents into active creators and problem-solvers.

Applications of agentic automation  

Agentic automation provides businesses with a customizable and adaptable toolkit to tackle their specific challenges. AI agents anticipate needs and act autonomously, effectively problem-solving. Here’s how the agentic approach is redefining possibilities across various sectors.

Healthcare: Improving patient care with real-time prioritization

Hospitals deal with thousands of patient updates daily, from test results to vital signs. Critical alerts, like a missing lab report or a high-risk condition, can get overlooked in the volume. Agentic automation addresses this by deploying systems that monitor health records continuously.  

An agent scans incoming data, cross-references it with patient histories, and flags priorities such as alerting nurses to urgent cases or escalating abnormal results to doctors.  

For instance, you can build an agentic system in Domo (Healthcare and Life Sciences) that could integrate with electronic health records to detect patterns like a sudden drop in oxygen levels. It could then trigger notifications via email or integrated apps to alert staff.

This reduces errors, shortens response times, and lets staff focus on care rather than monitoring. In practice, it also handles administrative tasks, like verifying insurance or scheduling follow-ups based on discharge criteria.

Retail: Agentic management (supply chain and in-store experience)

For almost any online retailer, customer satisfaction and operational speed are everything because delays in shipping or order issues can lead to lost sales. Agentic automation enables proactive management through automating tasks like tracking orders, updating statuses, and flagging problems.  

An agent monitors supply chain data in real time, detects delays from weather or inventory shortages, and acts, such as rerouting shipments or notifying customers of alternatives.

Similarly, in physical retail, a similar pressure exists on retail associates who struggle to juggle policies, promotions, and product details, which can lead to customer walkouts and lost sales.

Domo’s AI-powered retail agent employs agentic automation to transform the in-store customer experience. This intelligent solution provides instant and accurate answers tailored to the specific store environment. It achieves this by connecting various data sources, including store wikis, real-time promotions, training materials, and inventory data.

Associates can reduce customer wait times and answer questions accurately by utilizing the Domo agent. It ensures that employees can offer expert-level service from day one, without requiring extensive training.

HR: Simplifying onboarding for new hires

Onboarding new employees often faces paperwork delays, leaving hires uncertain and overwhelming HR teams. Agentic automation makes this process smoother by guiding it autonomously. An agent sends required forms, tracks completion, and prompts follow-ups across departments, such as IT for access setup or payroll for details.

It starts with an initial instruction, like “Onboard new hire X,” then pulls data from HR systems, verifies information, and flags incomplete sections or resends documents as needed. The agent coordinates with other systems, like training platforms, to assign modules based on the role. This cuts down on delays, ensures compliance, and frees HR personnel for more interpersonal tasks.

Customer service: One agent to rule the queue  

During peak times, support queues increase, resulting in slower responses. Agentic automation can help by scanning tickets, identifying priorities via keywords or sentiment, and providing instant resources. It resolves simple queries, like FAQs, and escalates complex ones to people.

An agent analyzes incoming requests, cross-references knowledge bases, and responds, such as resetting passwords or tracking orders. It adapts by learning from interactions, improving accuracy. This shortens wait times, handles volume spikes, and lets staff tackle nuanced issues.

Manufacturing and fleet management

The most immediate impact of agentic automation in manufacturing is the shift to predictive maintenance. Modern machinery is equipped with thousands of Internet of Things (IoT) sensors that generate a constant stream of data on temperature, vibration, pressure, and energy consumption. An AI agent can monitor that data in real-time, learning the unique operational signature of each piece of equipment.

When an agent detects a subtle deviation from the normal baseline, a microscopic increase in vibration in a robotic arm, for example, it recognizes this as a precursor to a potential failure.  

Then, the agent initiates an autonomous workflow to address the failure, while also sending an alert to the maintenance team and providing them with all necessary diagnostic information.

Also, during Domopalooza 2025, Koantek showed how they reshaped their fleet management system using agentic automation. They faced a logistical dilemma: Every delivery van off the road meant unhappy customers and lost dollars. Their old model meant waiting for breakdowns and then scrambling to react afterward.  

With Koantek’s new platform, powered by Domo’s Agent Catalyst and Databricks analytics, agentic automation now does the grunt work:  

• It monitors each vehicle’s performance for trouble signals, like abnormal vibrations.  
• If a high-risk issue is detected, like a critical wheel bearing about to fail, the agent triggers urgent alerts.  
• Simultaneously, it suggests the best route to the nearest repair facility, checks repair part availability, and transfers packages to another van nearby based on the driver’s location and current routes.  
• All this coordination happens in real time, slashing downtime and letting the fleet keep moving, even if one van drops out.  
• What once took a flurry of phone calls and stressed-out dispatchers is now handled, instantly, by smart, agentic automation in Domo.

Challenges and considerations

While agentic automation is seen as the ultimate performer in the data space, even the best have their weaknesses. Before implementing agentic automation, it's wise to be aware of common pitfalls, so you can avoid them.

Getting data quality right (or garbage in, garbage out)

Agentic process automation thrives on high-quality, up-to-date data. If your data sources are outdated, incomplete, or buried in chaotic spreadsheets, your agents will trip up. Establishing clean data pipelines is crucial; otherwise, your automations will struggle and lead to poor decisions.

Keeping humans in the loop (because someone should see the big picture)

While the idea of fully autonomous agents handling all tasks sounds appealing, it’s crucial to remember that agents, despite their proficiency with patterns, still require human oversight.  

Without a clear way for your team to set rules, fine-tune decisions, or intervene when a process goes off track, things can quickly spiral. Therefore, it’s essential to design systems where humans collaborate with their agentic colleagues, rather than merely supervising them.

Explaining agent decisions (and avoiding the “because I said so” problem)  

Sometimes, automated agents may exhibit behavior that appears illogical at times. When a deviations happen, teams want to know why. If our automated agents can’t explain their reasoning, user trust may erode, leading to a decline in adoption.  

So, transparency into an agent’s decision-making process is crucial, as people are much more likely to trust automation when they can easily understand the “why” behind their actions.

Navigating security and governance risks (without handing out skeleton keys)  

The more autonomy your agents have, the greater the risks if they make a mistake. If you give an agent permission to access private data or execute financial transactions, you need a strong governance framework from the outset.  

Make sure to implement strict access controls, maintain detailed audit trails, and involve your compliance team early on. With careful planning, you can ensure the only surprises your agents deliver are pleasant ones.

The future of agentic automation  

Agentic automation is a preview of what the future holds, and data leaders watch for, along with the new challenges and opportunities that digital agents introduce. Here’s how it's transforming the workplace:

• More autonomy, less micromanagement: Agents will handle bigger decisions, rerouting fleets or rebalancing inventory, and send you a handy summary when they’re done. ‍
• New partnerships between people and agents: Instead of replacing jobs, agents will take on tedious tasks, freeing people for strategic and empathetic work, allowing analysts to solve complex challenges. ‍
• Upskilling and workforce transformation: Organizations are investing in training data professionals to design and fine-tune automations. The focus shifts from “data wrangling” to innovation and hybrid human-agent teams.

Your next move: Bring agentic automation to life with Domo

Understanding the power of agentic automation is the first step. The next step is putting it into practice. With the Domo ecosystem, you can build, manage, and scale a workforce of intelligent digital agents. Through the Domo AI Service Layer, we provide the tools to move beyond static dashboards and into a proactive, automated action environment.

Here’s how Domo enables your agentic automation journey:

• Unify your data foundation: An agent is only as smart as the data it can access. Domo easily integrates with hundreds of data sources, from cloud databases to enterprise applications. This creates a clean, governed, and real-time foundation, so your agents operate with a complete and accurate picture of your business. ‍
• Build and deploy agents with ease: You don’t have to be a data scientist to create powerful agents. Domo's intuitive tools, like Agent Catalyst, let you configure and deploy agents for specific tasks with ease.  ‍
• Activate intelligent workflows: Domo agents can find insights and drive actions. You can build intelligent workflows that trigger alerts, update records in other systems, initiate communication, or escalate issues to the right people. They close the gap between analysis and execution, helping your business to move faster and with greater precision. ‍
• Govern with confidence: As you scale your digital workforce, security and governance are paramount. Domo gives you the tools to orchestrate, govern, and evolve your automation strategy at scale and within your organization’s compliance standards.

Ready to take the leap into agentic automation with Domo? Let’s get started today.