The critical role of data in building an AI agent
Data quality, modern tooling and AI governance are key to building successful AI agents that meet user expectations, writes industry analyst Stephen Catanzano.
- Stephen Catanzano,
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Enterprise Strategy Group
We provide market insights, research and advisory, and technical validations for tech buyers.
Building an AI agent is a complex yet fascinating endeavor, requiring advanced algorithms, substantial compute resources and -- most importantly -- high-quality data.
AI agents, whether chatbots, recommendation systems or autonomous vehicles, must be able to process inputs; deliver outputs, including actions; and learn and improve over time. These capabilities rely heavily on the data used during training and operations. The quality, relevance and integrity of that data are critical to the AI agent's performance and ability to take effective actions.
According to research from Enterprise Strategy Group, now part of Omdia, customer service is the top use case for generative AI. Organizations across industries are building agents to assist customers with inquiries, troubleshoot issues and even upsell products.
On the surface, these agents might seem simple, but their performance depends on strong data foundations. A key difference between a chatbot that merely answers questions and an AI agent is the agent's ability to reason, learn and act.
For example, an advanced customer service agent can validate a user, recommend a product based on purchase and search history, and take actions. For example, an agent might ask, "Would you like me to add this to your shopping cart?" and then follow up with “If ready, would you like to ship the item to your home address and charge it to your card ending in 1234?"
For a customer service AI agent to function effectively, it needs historical training data and access to current systems. The diversity and quality of these data sets are critical. If an agent's training data skews toward specific inquiries, contains inaccuracies or fails to include more nuanced customer concerns, the agent will struggle to provide helpful answers, thereby frustrating users. For instance, an e-commerce agent without sufficient data on returns or refunds will fail to assist customers with those common requests.
The role of data tools in maximizing value
Organizations must take advantage of modern data tools, such as data lakes and databases, to extract the maximum value from data. Data lakes enable storage of vast amounts of source data, which can be used for AI training and analytics. Databases, on the other hand, are critical for storing processed and structured data that is frequently accessed and queried.
Different types of databases -- such as document, graph, time-series or relational -- combined with tools like vector search and retrieval-augmented generation all play an important role in feeding AI models with relevant context. They are the foundation for successful AI agents.
For instance, an agent might rely on a database to access up-to-date product information, inventory levels or frequently asked questions. By combining the power of data lakes for exploratory analytics with the operational efficiency of databases, organizations can ensure that their AI agents can access both historical and real-time data as needed.
Ensuring data quality, accuracy and governance
No matter how powerful an algorithm is, low-quality data will lead to subpar results. Organizations looking to maximize the effectiveness of AI agents must invest in tools and processes that ensure data quality, accuracy and governance.
Data validation frameworks can automate the detection of errors, inconsistencies and duplicates in data sets. These tools help ensure that agents are not trained on outdated or erroneous information, which could lead to incorrect responses.
Solid data governance is also essential for controlling how data is collected, processed and accessed in accordance with internal corporate governance policies and regulations. AI governance extends this framework to manage the output from AI -- for example, evaluating whether responses to users' prompts meet governance and ethics standards. This protects customer privacy and reduces the risk of legal and reputational damage.
Data lineage and tracking tools are also crucial, enabling organizations to understand the origin, transformation and flow of data throughout their systems. This is particularly important for AI agents that continuously learn from live data. If an agent suddenly begins making errors, tracing the issue back to a specific data source or preprocessing step is critical for resolution.
Building an AI agent in a context like customer service highlights the critical role of data in achieving success. From collecting representative and high-quality data sets to using modern tools, organizations must treat data as the foundation of every AI initiative.
By prioritizing data excellence, organizations can develop AI agents that meet user expectations and deliver measurable business impact. Companies like Amazon Web Services, Oracle, Google Cloud, Microsoft Azure, Cloudera, Quest and Elastic are constantly innovating to provide the trusted data foundations necessary for successful AI, including AI agents.
Stephen Catanzano is a senior analyst at Informa TechTarget's Enterprise Strategy Group, where he covers data management and analytics.
Enterprise Strategy Group is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.
