Agentic AI is emerging as a transformative technology in enterprise operations. In this article, we’ll give you an overview of what AI agents are, go over some enterprise use cases for agentic AI, and explain why the quality of your data is the primary factor influencing the success and ROI of your AI initiatives (agentic and otherwise).

What is agentic AI?

As we wrote on the Stack Overflow blog recently, the terms “agentic AI” and “AI agents” refer to autonomous AI systems that make decisions to achieve specific goals with minimal need for human oversight and intervention. As AI expert Enver Cetin told the Harvard Business Review, agentic AI systems understand the user’s goal or vision and the context behind the problem they’re tasked with solving. Here’s a simple way to think about it: Generative AI creates content, while agentic AI solves problems on behalf of a user.

AI agents unlock a world of potential use cases for the enterprise. Here are just a few, drawn from conversations we’ve had with customers and partners:

• Helping healthcare providers get paid for their work with less back-and-forth with insurance companies
• Testing complex code at scale for better test coverage and code quality
• Upgrading Java across a humongous codebase, saving 4,500 years of developer time
• Reviewing code and writing pull requests to give developers more bandwidth for higher-order work
• Automating developer workflows to increase productivity and boost developer happiness

While agentic AI systems are capable of following complex sequences of interconnected tasks, that also means more possible points of breakdown if the underlying data isn’t accurate, reliable, or useful for your use case.

AI agents are only as good as their training data

The output of agentic AI systems—AI capable of autonomous decision-making and reasoning—depends on the data they are trained and informed by. Poor-quality data undermines even the most advanced systems, while high-quality data unlocks their full potential.

Expert research has shown that data quality is the primary factor influencing the performance of a large language model (LLM): Models trained on up-to-date and well-organized data deliver more accurate, complete, and relevant answers than models trained on lower-quality data. In the same vein, research from the MIT Media Lab has found that integrating a knowledge base into a model improves output and reduces hallucinations.

To be successful in solving business problems for your organization, agentic AI systems need data that is:

• Accurate: Accuracy serves as the cornerstone of an AI system's performance. Reliable data allows agentic AI to make correct decisions and avoid costly mistakes. Any inaccuracies in the data can mislead the model, resulting in flawed decisions or incorrect outputs.
• Structured and organized: AI systems thrive when data is structured systematically, making it easier for those systems to draw sound conclusions by analyzing and connecting information. For example, research from Cornell University has shown that a dataset structured around questions and answers, like the one we’ve built over many years of developer knowledge-seeking and -sharing at Stack Overflow, helps train a model to provide useful answers to specific questions. Structured data formats allow the model to quickly locate the information it needs and place it in context. Metadata tagging is another way of structuring data: attaching relevant context to make it easier for the AI system to retrieve and interpret information.
• Up-to-date and dynamic: In fast-changing environments, obsolete data introduces risks: people and systems make suboptimal decisions if they don’t have access to the latest, most accurate information. Keeping information up-to-date allows agentic AI systems to mitigate these risks and ensures that the agent can respond to new inputs with the proper context.

Ensuring high-quality data can be a resource-intensive process if you don’t have the proper tools in place to capture, preserve, and maintain that data. But try using poor-quality data to drive your business decisions instead, and you’ll realize that’s much more expensive. Low-quality data can result in:

• Hallucinations: AI systems may generate outputs that are incoherent, incorrect, or misleading, undermining their usefulness and eroding user trust.
• Inefficient workflows: When data is fragmented or incomplete, AI systems struggle to make informed decisions, causing delays and inefficiencies.
• Loss of user trust: Repeated instances of inaccuracies or irrelevant outputs create skepticism, making users hesitant to rely on AI-powered tools and diminishing the return on investment for such systems.

Practical steps to get your data ready for use in agentic AI applications

Ensuring data quality is, of course, a complicated and multidimensional task that requires input and investment from teams across your organization. But at a high level, there are some practical steps you can take to get your data into a place where agentic AI systems can leverage it to deliver tangible benefits for your organization:

• Assess your current data quality and accessibility: Many organizations assume their data is both comprehensive and accessible, only to find in the midst of an AI initiative that gaps exist in quality, structure, and labeling. Centralizing institutional knowledge into a single, clean, and accessible repository reduces silos and fragmentation, so you don’t encounter unpleasant surprises as you roll out AI projects.
• Prioritize quality over quantity: Bigger isn’t always better. Data quality is crucial for model accuracy and efficiency. The recent shift toward smaller, high-performing models spotlights the benefits of training models on highly refined, relevant data.
• Build a collaborative knowledge base: A universal challenge organizations face is capturing and maintaining institutional knowledge, especially as AI becomes increasingly integrated into enterprise operations. Without a unified knowledge base, valuable insights remain fragmented or lost across departments, leading to inefficiencies and duplicated work.

• Keep things fresh: Integrating systems that refresh or modify data dynamically allows AI to work with the latest information, reducing risks associated with outdated content.
• Structure and organize your data: As we mentioned above, data structure and organization makes a big difference in how usable your data is to an AI model. Metadata tagging enhances the usability of datasets by improving searchability and retrieval.
• Develop data governance and compliance standards: Organizations need a strong governance framework around their internal data. As AI regulations tighten, maintaining compliant, well-documented data practices becomes crucial.

If you’re looking for a more comprehensive guide to getting your (data) house in order to support AI initiatives, you can find it in our Resource Center here.

Data fuels agentic AI

To reiterate, agentic AI systems need a centralized, well-structured source of data to deliver consistent results. The autonomy of agentic AI systems—that is, the extent to which they can add value to your organization while acting relatively independently of human oversight—depends on the quality of the data they consume. High-quality data allows these systems to make well-informed, context-rich decisions with minimal human oversight. High-quality data that reliably produces results also fosters developers’ trust in agentic AI.

Clean, structured, centralized data is much more than a technical requirement: it’s the strategic foundation for autonomous AI solutions. Orgs that prioritize data quality in the emerging era of agentic AI will be better-positioned to harness this tech to deliver greater value to their customers and their teams.