AI agent applications are poised to transform the way employees and customers interact with computer systems. By gathering data, reasoning and executing tasks, these agents will automate human workflows across countless current use cases — from internal support bots to sophisticated customer-facing services — across every industry.
Some are looking toward analytical databases to power these agents. However, AI agents are fundamentally operational in nature, much like traditional web applications, mobile apps and microservices. Recognizing this distinction is critical for selecting the right data platform.
What Are AI Agents and How Do They Work?
Unlike legacy applications restricted to rigid inputs and predefined logic, AI agents dynamically interact with their environment. They continuously receive and process data from multiple sources, perform real-time reasoning and autonomously execute tasks. They use various tools, functions and system prompts to retrieve relevant data, ask the next questions, refine their reasoning and take action.
Because agents rely on large language models (LLMs) or other advanced machine learning techniques, they need to operate in real time and require a data infrastructure that supports frequent, low-latency read and write operations. This is why an operational database — one designed for immediate, ongoing interactions rather than delayed batch processes — is essential.
The Data Sources – A Retail Example
A typical AI agent in a retail setting might use a wide array of operational data, combining both classic and AI-specific data. Here are just a few examples:
- User profiles and preferences: Enables hyper-personalized recommendations and customer experiences.
- Product catalogs with media: Allows for a richer user experience.
- Inventory data: Ensures items are in stock and conveniently located for fulfillment.
- Web calls and external APIs: Agents can retrieve additional information — such as celebrity associations, contextual reviews or market sentiment — to enhance recommendations.
- Historical sales data: Supports upselling, cross-selling and predictions of what a customer might purchase next.
- Unstructured content: Documents such as PDFs detailing product usage or care instructions can be integrated to improve the quality of agent responses.
- Vector embeddings: Required for semantic search and retrieval-augmented generation (RAG), drastically improving LLM responses without retraining custom models.
Agentic AI also needs to maintain information on the tools and functions that developers create. This metadata helps the agent choose which function or data source to invoke, continuously evolving its skill set. Additionally, semantic and conversation caching allows the agent to reuse existing context, improving speed and reducing costs by minimizing repeated requests to expensive LLM endpoints.
As prompts and tools evolve, the system must capture and maintain the history of interactions, including transcripts, decisions and intermediate reasoning steps. For other industries, the core operational data types may differ (for instance, sensor readings in manufacturing), but the underlying principle remains the same: AI agents require a capable operational database that can handle diverse data formats, frequent updates and real-time accessibility.
Why an Operational Database Matters
Using multiple, disparate technologies — one for caching, another for vector search and another for transactions — can degrade performance, hinder management and complicate data governance. For AI agents to deliver timely results, all these data interactions must occur with minimal latency.
Operational databases excel at high-velocity, high-concurrency workloads that demand real-time reads and writes. They also typically offer robust replication and clustering features to ensure high availability, which is critical for AI-driven applications that must remain responsive.
Conclusion
AI agents are set to become a cornerstone of modern computing. Choosing a platform specifically designed for speed, scalability and low-latency interactions ensures AI agents can effectively gather, process and act on information — delivering robust, context-rich experiences to end users. By their very nature, they generate and rely on real-time data, making operational databases a necessity for AI agents.
Couchbase AI Services have been introduced by Couchbase to securely interact with AI models, incorporate unstructured data and provide out-of-the-box semantic and conversation caching. The Agent Catalog feature makes it easier for developers to discover, manage and secure the tools and functions an agent needs, integrating seamlessly with popular AI frameworks such as LangChain. Additionally, Couchbase’s multipurpose database eliminates the need to use multiple, single-purpose databases, reducing the complexity and costs of application development. Find out more here.
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A platform specifically designed for speed, scalability and low latency ensures AI agents can effectively gather, process and deliver context-rich results.