Real-World Use Cases of AI Agents Across Industries

AI agents are rapidly becoming integral to our professional lives. They handle repetitive tasks such as schedule management, research, and report writing, or find and analyze the information you need and deliver organized results. With AI assistants like ChatGPT, Claude, and Microsoft Copilot working around the clock, many professionals are seeing significant gains in productivity.

But what if AI agents could go beyond routine office tasks and develop deep expertise in specific domains?

Across industries such as manufacturing, supply chain, defense, and data quality management, AI agents are already supporting enterprise decision-making and improving operational efficiency.

So how are companies in each field actually applying AI agents to their day-to-day operations? In this article, we will walk through some of the most representative AI agent use cases. We have also prepared special insights for AI agent developers, so be sure to read to the end.

AI Agent Use Cases by Industry

Manufacturing Companies

Predictive Maintenance

On the manufacturing floor, countless pieces of equipment and production processes run simultaneously. If even one machine suddenly goes down, the result can be production disruptions and financial losses, even if the downtime lasts only a few hours or a single day.

AI agents can prevent these failures by monitoring equipment conditions in real time before a breakdown occurs. AI agents built for manufacturing continuously analyze data from sensors attached to equipment to detect early signs of anomalies.

For example, if a particular machine shows unusual patterns in temperature, vibration, or power consumption, the agent predicts the likelihood of failure and alerts the responsible personnel.

Product Classification

For manufacturers producing a wide variety of products, accurate product classification is critical. When production volumes are high or the same product comes in many different shapes, manual inspection and classification quickly reach their limits.

• In one real-world case, a major corporation was using a recycling AI to sort waste by material, but struggled to improve model performance due to data quality issues.

• For instance, even items made of the same plastic varied widely by material type, and the AI failed to properly recognize transparent or light-reflecting objects.

• In addition, even slight deformations in shape caused the AI to treat items as entirely new, unseen objects.

To address this, the company improved the quality of the data behind its recycling AI using the Data Clinic platform and PebbloSim. The platform was tasked with analyzing data bias and identifying gaps in the training data, enabling the existing AI to learn from recyclables in diverse environments and forms. This kind of quality work is now being automated end to end by AADS, the agent technology built into the first release of Data Greenhouse.

As a result, the company was able to secure data that more closely resembled real-world conditions with greater efficiency, and significantly improved the recognition performance and reliability of its recycling AI.

Defense Agencies and Companies 

Command and Decision-Making Support

In the defense sector, fast and accurate decision-making is unquestionably paramount. Yet the battlefield generates massive volumes of real-time data from drones, satellites, radar, communications equipment, and many other systems. It is simply beyond human capacity to analyze and evaluate all of this information.

No matter how seasoned a commander's judgment may be, that judgment loses its power without sufficient real-time intelligence on the battlefield.

This is where AI agents can help. They connect information from multiple systems, analyze it holistically, and summarize the battlefield situation. They can support command by identifying risk factors and priority response tasks. They can also serve as decision-support tools that propose courses of action based on multiple scenarios.

Target Detection

On the battlefield, accurately detecting targets, whether friend or foe, is essential to gaining the upper hand. As the defense sector undergoes its own AI Transformation to achieve this level of precision, a growing number of agencies are adopting defense AI.

However, building AI in the defense sector presents unique challenges compared to other industries. First, security constraints make it difficult to collect real-world data. Acquiring data that reflects diverse environments also demands significant time and cost.

In particular, if conditions such as season, weather, terrain, or imaging distance change even slightly and the AI fails to recognize the target, serious concerns can arise in actual operational environments.

To solve this, one company used the Data Clinic platform together with Pebblous' synthetic data generation technology, which has since been productized as PebbloSim. The platform enabled the AI to learn from drone imagery captured in diverse environments and helped build datasets that closely mirror real-world conditions. As a result, the project achieved meaningful improvements in the AI's performance and reliability.

IT, Commerce, and Every Company That Works with Data

Data Analysis and Insight Generation

Raw data is like an uncut gemstone: valuable only once it is refined. In other words, no matter how much data a company accumulates, it is meaningless if no insights can be drawn from it.

However, when data is scattered across multiple systems or the volume of data to analyze is massive, it takes a significant amount of time for people to manually review the data and derive meaningful results.

This is where AI agents come in. They can connect multiple data sources to analyze data quickly and detect anomalies or key patterns. They can also summarize analysis results and deliver the insights decision-makers need directly to the responsible personnel.

For example, an AI agent can analyze the causes behind a decline in sales for a specific product, identify customer segments with a high risk of churn, or proactively detect data quality issues.

Continuous Data Quality Management

The quality of your data determines the quality of every outcome built on it. This makes quality management essential, yet by the very nature of data, it cannot be completed in just one or two rounds of maintenance.

What matters is not just diagnosing and improving data once, but continuously observing, improving, and operating it over time.

• For companies pursuing AI Transformation and building their own custom AI agents, data quality management becomes even more critical. AI agents make judgments and take actions based on internal enterprise data, and if that data is of poor quality, the AI agent is far more likely to make flawed decisions.

• Moreover, diagnosing and improving data quality has traditionally required the specialized analysis and repetitive work of data scientists. Companies without in-house data experts often struggled to continuously operate and optimize their data, even after adopting data solutions to fill the gap.

That is why a new approach is emerging: having AI agents perform data quality management itself. The AI agent continuously monitors data, discovers quality issues, and analyzes the areas that need improvement.

To make this possible, Pebblous, a data infrastructure company, developed AADS (Agentic AI Data Scientist), an autonomous AI data scientist. AADS is an AI agent specialized in data quality management with the expertise of a professional data scientist, autonomously executing the entire data management lifecycle from quality assessment to data improvement and re-evaluation.

• AADS runs within an environment called the Data Greenhouse. The Data Greenhouse is an AI agent-based data operations environment where AADS diagnoses and improves data, PebbloSim generates the missing data, and the results are verified once again.

• When a user uploads a dataset, AADS first analyzes its structure and characteristics. During this process, it automatically extracts the ontology and data structure from the dataset and independently selects the Data Lens best suited to the data's characteristics. It then diagnoses duplicates, errors, bias, and data gaps before determining the appropriate improvement strategy.

• AADS also reduces unnecessary data while preserving dataset quality, performing what we call the Data Diet function. It analyzes the impact on AI model performance and autonomously determines which data to keep and which to trim. This allows companies to keep their data footprint only as large as needed while improving both training performance and operational efficiency.

• In the actual prescription process, AADS compares and analyzes multiple candidate configurations and even predicts performance changes to propose the optimal data composition.

• The AI agent also automates data bulk-up and synthetic data generation. This is where PebbloSim comes in. PebbloSim is an AI agent-based synthetic data generation tool that runs on top of the Data Greenhouse.

• Based on the analysis results from AADS, it generates precisely targeted training data reflecting diverse environments to fill data gaps, helping AI models perform reliably in real-world operating conditions.

• Even after improving the data once, the AI agent re-evaluates data quality and improves it again. In keeping with the ever-changing data landscape, it manages data quality on a continuous basis.

• Pebblous is also continuously advancing its autonomous assessment capabilities built on the ISO/IEC 5259-2 data quality evaluation framework.

• Furthermore, research is underway to enable the AI agent to perform quality assessments at the KOLAS accreditation level, allowing companies to establish a system for continuous monitoring and improvement rather than one-off data quality checks.

The principles matter, but what truly counts is the results it delivers.

AADS was in its early stages not long ago, but it is a different system today. Its performance has advanced to the point where the entire workflow, from dataset download to diagnosis, Data Diet, and re-diagnosis, is executed fully autonomously by the agent.

While the manufacturing and defense cases above were delivered with the Data Clinic platform and Pebblous' synthetic data technology, AADS is the agent technology developed under a three-year national R&D program, selected for the Global Big Tech Development Program by South Korea's Ministry of Science and ICT, and its first release now ships inside Data Greenhouse. The following are key results achieved in the first year:

• Achieved an internal data quality index score of 88, derived from ISO/IEC 5259-2 measures

• 95% success rate across more than 100 tasks

• Achieved an 80% compliance rate with ISO 42001 Annex A logging requirements as an interim 1st-year milestone

• 30% reduction in data quality management work hours

We are also sharing select real-world enterprise cases where synthetic data was generated through PebbloSim, our AI agent-based synthetic data generation tool:

• Company A (Mobility): Reduced data acquisition time from 15 days to under 1 hour, improving AI model detection accuracy by approximately 200% in a benchmark subset using Data Clinic and PebbloSim synthetic data.

• Company B (Virtual Idol): By trimming the training set to about 15% of its original size using Data Diet techniques, cut training time from one week to a single day—roughly 7x faster.

What Many Miss While Focusing Only on the 'AI Agent' Itself

As you read through these AI agent use cases, you may have naturally focused on the AI agents themselves. More and more companies are pursuing AI Transformation and looking to adopt or develop AI agents tailored to their business.

Behind every AI agent delivering real results across industries, there is one common foundation: healthy data.

When Pebblous analyzed this problem, we concluded that while the AI model itself can be a factor, the issues frequently originate from data quality. AI agents make judgments and take actions based on a company's data and knowledge. What happens if the data underpinning the AI agent is insufficient, or riddled with duplicates and errors? The AI agent's performance will inevitably suffer.

How Should You Manage Data Quality to Build a Trustworthy AI Agent?

1. You Must Connect the Relationships Between Data

Developers building AI agents often ask us questions like these:

"The AI agent we just built keeps making errors. What should we do?" "We have plenty of data, so why isn't our AI agent working properly?"

The answer lies here. As a simple example, suppose you assign the following task to an AI agent for a logistics supply chain:

"Find orders at risk of shipping delays, prioritize them, and take the necessary follow-up actions."

To complete a task like this, AI agents work through multiple steps, such as checking inventory, analyzing the likelihood of shipping delays, and sending alerts to the responsible personnel.

If the AI agent looks at only a single piece of data, it cannot figure out how to solve this task. In other words, looking only at the customer's name, or only at follow-up strategies in isolation, is not enough to complete the job.

The AI agent can only understand the current situation by comprehensively analyzing customer, order, product, and inventory information. Then it handles the task like this:

• The product ordered by the customer is likely to be delayed by about 5 days due to insufficient inventory.

• An additional purchase order is needed for the product, so I will proceed with the reorder.

• I will send the customer an apology message for the shipping delay along with a discount coupon.

As this example shows, an AI agent can only do its job correctly when it is given not just individual data points, but a clear understanding of the relationships between them.

2. It Must Also Understand the Meaning and Context of Data

For an AI agent to make accurate judgments, it must also be able to understand the meaning and context behind the data.

Within an enterprise, for example, different departments may refer to the same product by different names, or the same customer may be managed in different ways across multiple systems.

Let's get more specific. The term 'approval' used inside a company can carry different meanings depending on the situation:

• Purchase approval

• Security approval

• Payment approval

What happens if you ask the agent to approve a specific task, and it fails to understand the context and grants a security approval instead of a purchase approval? Your security could be compromised.

Humans can naturally distinguish between these through context, but an AI agent may interpret them incorrectly if the criteria are not defined. Human context must be taught to the AI agent.

The Solution Is 'Ontology'

Yes, this is the very concept that enterprise companies around the world, including Palantir, are focusing on in AI development.

If you want to improve your AI agent's performance, building an ontology is essential. But simply creating an ontology does not automatically make your AI agent smarter.

You must continuously verify whether any relationships between data are missing, whether business context is properly reflected, and whether the terms and concepts actually used in the field are accurately defined.

Only through this process can an AI agent understand a company's business processes and make judgments with human-like consideration of context.

As demonstrated throughout this article, Pebblous has released the first version of Data Greenhouse, powered by AADS, together with a working prototype of PebbloSim, both developed through a three-year national R&D program, while serving its enterprise clients through Data Clinic.

If your company is pursuing AI Transformation and planning to build a custom AI agent, we encourage you to first assess whether you have trustworthy data and a well-defined ontology in place.

If you adopt an AI agent without the data environment it needs to work properly, the agent will only produce errors.

If you are weighing AI agent adoption and data quality management, we invite you to diagnose your current data environment with Pebblous, a data infrastructure company building AI agents grounded in real enterprise data work. As a company with deep expertise in both AI agents and data, we will show you the best possible solution.