Vector Databases: The Foundation of AI Agent Innovation

Vector database growth shows no signs of slowing in the year and years ahead, with Forrester predicting that most organizations will have vector databases in production by 2026. However, vector databases and their challenges will change dramatically, especially when used at scale. Today, this is especially apparent in the role vector databases are playing in the enterprise adoption of AI agents.

Vector databases will be key to building AI agents, the “next frontier of generative AI,” according to McKinsey. “We are beginning an evolution from knowledge-based, gen-AI-powered tools — say, chatbots that answer questions and generate content — to gen AI-enabled ‘agents’ that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action,” a recent McKinsey Quarterly article states.

Deloitte predicts that 25% of enterprises using GenAI will deploy AI agents in 2025, growing to 50% by 2027. “While early adopters will grapple with complexities and challenges, the vision is compelling enough for organizations to take proactive steps to prepare themselves now for adoption,” according to Deloitte Global’s 2025 Predictions Report, Generative AI: Paving the Way for a Transformative Future in Technology, Media, and Telecommunications. “This evolution will enable AI agents to tackle a broader range of applications, providing businesses with valuable tools to drive the productivity of knowledge workers and efficiency gains in workflows of all kinds.”

Agentic AI Is a Job for Native Vector Databases

While traditional databases can support AI applications, they lack the specialized architecture to efficiently process vast volumes of multimodal, unstructured data — notably, in real time. Native vector databases are uniquely suited to tailor the relevant and contextually aware responses that agentic AI demands.

With many traditional applications, for example, users send a request and get an answer based on finite and structured data. Agents are very different in making decisions based on various inputs across many steps, using various data types. In healthcare, for example, an AI agent might synthesize the latest X-ray images, physician notes, lab results, research papers, and more to act as an assistant in clinical settings, working with human insight and adjusting recommendations as data evolves. In the travel industry, an AI agent could develop personalized itineraries using real-time data from social media posts, photos, videos, travel guides, news feeds, and weather reports.

The possibilities are endless, but trust is the common factor in any AI agent’s success. Vector databases act as memory for these agents, enabling adaptive learning, real-time decision-making, collaboration among agents, and contextual precision.

QA.tech, which provides AI-driven automated testing solutions, uses the Qdrant vector database solution to enable testing agents that perform tasks on the browser just like a user would. These agents replace hard-coded tests that are challenging to set up and maintain over time and obviate the need to hire human QA testers. The QA.tech agents document errors and flag errors for developers to review. Because the entire testing process is recorded, developers can quickly analyze each step to identify problems and gaps.

QA.tech’s requirements — most notably, efficient real-time operations and scalable infrastructure — mirror the needs of most enterprise use cases. As enterprises adopt and expand the use of AI agents — and as the AI agents themselves are built to perform increasingly complex tasks — it will be essential to consider how and how well a vector database manages network overhead and CPU load, as well as its ability to store multiple embeddings for different use cases.

Challenges to Vector Database Expansion 

While vector databases have tremendous potential for enterprises looking to adopt agentic AI, they also present challenges.

For example, as the use of agentic applications expands, so will concerns about data leakage, data sovereignty, and regulatory compliance. Open source systems, role-based access control, encryption, and the ability to flexibly deploy databases on-premises, in data centers, and/or in the cloud are just some of the tools organizations can use to ensure that vector databases don’t become a weak link in the AI application chain. Cost and capacity will also be a concern as vector databases scale, which is another reason for evaluating open solutions that provide hybrid deployment options.

Another concern is available skills. Many companies do not have the expertise to get vector databases up and running, let alone to fully optimize agentic AI technology in an enterprise setting. To be fair, this is less of a vector database issue and more of an AI issue — the technology is evolving rapidly, as are the use cases, and it can be challenging to keep pace. Agentic AI, for example, might not have been on enterprises’ radars six months ago but is now, for many companies, a critical competitive requirement.

Organizations must dedicate time and resources specifically to AI and vector database technology. Still, they can also close the skills gap by focusing on using open source throughout the AI stack. Organizations using open source solutions benefit from the community’s collective knowledge and experience but also avoid the issues of using AI products—that lack transparency.

If 2024 was the year that organizations discovered the synchronicity between vector databases and gen AI, 2025 and beyond will be the era in which vector databases drive gen AI innovation, including agentic AI.