In the prolific, multi-trillion dollar arms race over artificial intelligence technology, DevOps are experiencing a game-changing boost. Unquestionably, machine learning is reshaping the world one industry at a time and database management is no exception.
How are AI agents used in database automation?
Today, agentic AI can autonomously provision, configure and optimize databases, fundamentally transforming the way in which they are managed and run.
While AI technology continues to develop at a shocking and breakneck pace, DevOps becomes even more critical to ensure these fast-moving innovations aren’t left alone to run amok.
“AI will accentuate what you’ve got going on, including bad stuff — or good stuff, because AI doesn’t care,” said Jon Kern, software developer and agile consultant at Adaptavist.com.
What is platform engineering and how does it relate to DevOps?
Most critically, platform engineering is enabling AI-driven DevOps to be both secure and scalable. Platform engineering is the practice of designing and building self-service infrastructure and standardized workflows for internal development teams. For humans, it is abstracting the most complex and technical aspects of cloud infrastructure, reducing friction and making it possible for teams to deploy software much quicker and easier. For AI, it’s implementing crucial guardrails for agents to operate within.
For example, an AI agent can deploy tried and tested templates instead of being forced to reinvent the wheel with every query. Those agents can then safely interact with existing infrastructure and systems using established tools.
Platform engineering is also essential to limiting AI agents’ potential mistakes by setting permissions around enterprise policies like governance and security. Finally, it can provide a framework to evaluate agent actions and see if their work was successful.
That’s why 87% of IT professionals report that AI is shifting engineering tasks away from manual coding and toward internal system design and guardrails.
“In the database world, it's an exciting time with tremendous innovation,” said Ashish Mohindroo, general manager and senior vice president with Nutanix, who leads the team behind Nutanix Database Service technology. “AI is really pushing that envelope, but now you have to manage all kinds of databases in parallel.”
As a result, adoption of agile DevOps development techniques are racing ahead. According to Perforce’s 2026 State of DevOps Report, 70% of organizations say DevOps maturity materially affects AI success.
But there’s a reason for caution. Databases manage data. Sometimes critical or even regulated data, like financial data and personally identifiable information. It is the database administrator’s job to ensure all databases – whether used for development purposes and production use cases, deployed on-premises or in the public cloud, relational and non-relational in nature – are performant, scalable, available, secure and backed up.
Explosion of Choices
Performing all those tasks manually is no longer realistic. Not only does software development move faster today, but there are also many more databases to choose from.
Not long ago most applications were built for relational engines like Oracle or MySQL. But in recent years a profusion of NoSQL databases like key-value stores, document databases, graph engines, spatial and time-series databases have created myriad new options.
“There’s been an explosion of databases, and developers have more choices than ever,” said Cesar Rojas, Nutanix’s Database Service Product Marketing Lead. “They want to use the ones that make the most sense for their applications.”
It isn’t uncommon for even a small mobile application to touch three or four databases. In a microservices environment, each service has a database, and there can be thousands of them loosely coupled together.
And it’s not uncommon for even mid-sized organizations to have hundreds, even thousands of databases running across on-premises data centers and one or more public clouds. Database automation makes it possible to manage that growing complexity, with solutions like Nutanix Database Service (NDB) delivering DevOps database automation at scale.
Essentials to Ease Database Bottlenecks
Traditional approaches to database provisioning and maintenance have become a bottleneck for agile software operations. The high levels of automation that DevOps teams enjoy need to trickle down to the database layer, according to Rojas.
He pointed out four essential features developers need.
Self and AI-agent-driven provisioning – Developers have been provisioning compute for years. Developers should be able to launch a database as quickly as they spin up a virtual machine or container. NDB delivers true self-service provisioning in minutes via a unified console, APIs, or integrations with 3rd party tools. DBAs define policy-driven profiles and golden images, while developers get instant, compliant database instances without tickets or delays.
But there’s also been a rapid shift away from self-provisioning. In the age of AI, agents can autonomously decide, plan and execute how to best provision a database while staying within the bounds of its specific permissions. Traditionally, a person would have to manually and painstakingly make selections at every point of the workflow, whether that’s choosing servers, networks or specific configurations. Instead, AI agents can be deployed to automatically and dynamically orchestrate the entire process, as they intelligently reason through every possible decision point.
Automated patching and backups - Patching is a mundane but necessary task that no DBA enjoys. Patching a dozen different database instances takes hours and often must be done late at night or on weekends to minimize downtime. And for e-commerce companies, any downtime is a problem.
An automated patching solution should not only keep fixes up to date, said Rojas, but track change histories and provide an audit trail for quality control and compliance purposes.
Backups are another necessary evil. Depending on whether they’re created locally or in the cloud, a 100-GB database backup can take up to 48 hours and require human oversight to monitor for failures. Restarts can significantly increase these times. NDB automates bulk patching with maintenance windows and full audit trails, while leveraging efficient storage snapshots for near-instant backups, clones, and point-in-time recovery—even for large databases—dramatically reducing operational overhead in DevOps database automation.
Database customization - Developers need access to many different types and configurations of databases depending on the applications and microservices they are building. It is important that automating database administrative tasks doesn’t come at the expense of the ability to customize database servers. DBAs and developers need to retain the flexibility to choose the right operating systems, database versions, and database extensions to meet application and compliance requirements. NDB supports this with customizable service profiles for multiple engines (Oracle, SQL Server, MongoDB, PostgreSQL, MySQL, and MariaDB), while preserving full control over OS, versions, extensions, and HA configurations.
Cross-environment portability - Modern enterprises run their databases across a wide variety of different infrastructures, whether that’s on-premises, private or public clouds, and increasingly, Kubernetes environments. Database automation tools allow every step of every workflow, such as provisioning, monitoring or backup to be run through an unified management layer across all of these environments. Kubernetes also increases portability by providing a consistent interface to orchestrate containerized database workloads across different infrastructure platforms. Combined with other tools, Kubernetes enables teams to automate database provisioning and configuration with repeatable workflows.
Governance - In the era of machine learning, governance becomes even more paramount. In particular, with AI agents independently and autonomously creating and operating databases, it’s pivotal that their access to sensitive data is capped and their permissions are limited to what they are allowed to do without human intervention. Security must also be maintained whether that’s through the enforced use of encryption or secret handling. Regulatory compliance, such as how vulnerable data is properly handled, must also be hardwired into every system. That is why platform engineering has become so critical to the safe, thoughtful and controlled deployment of AI agents.
The Database-as-a-Service Option
Many of the capabilities just described are available in managed databases or database-as-a-service (DBaaS). Nutanix Database Service (NDB) goes further as a comprehensive DBaaS platform—not just a cloud service—bringing cloud-like simplicity and automation to on-premises and hybrid environments while maintaining full control and compliance.
Cloud DBaaS is a good option if developers are working only with applications and databases running in a single cloud. However, the reality is that most organizations run applications and databases across more than one cloud, including private cloud and on-premises datacenters.
“Customers will have some workloads that stay on-prem, some that go to the cloud, and some that may run in colocation facilities or edge locations,” Rojas said. “A DBaaS that works with only one cloud has limited value.”
For DBAs and developers running databases in two or more public clouds, they will need to work with two or more cloud DBaaS. A limitation of working with multiple cloud DBaaS is that each provider has its own administrative dashboard and provisioning features. Each also has its collection of plug-ins for tasks such as monitoring agents that must be enabled and configured individually.
Furthermore, on-premises applications aren’t going away anytime soon. In some highly regulated industries, for the foreseeable future, most applications will be deployed on local infrastructure. Cloud DBaaS is of no help there.
In addition, most organizations today run multiple types of databases. Many enterprise applications use commercial databases like Oracle and SQL Server, while developers often choose open source databases like PostgreSQL and MongoDB to support new, modern applications. For organizations running multiple databases, this can add up to significant complexity. Nutanix Database Service (NDB) goes further as a comprehensive DBaaS platform bringing cloud-like simplicity and automation to on-premises and hybrid environments while maintaining full control and compliance.
Support for multiple database types is also critical. This enables fleets of relational and NoSQL engines to be managed from a single console and application program interface. However, DBAs still have the flexibility to define custom services and install extensions on individual databases. NDB not only simplifies the DBA’s job but provides developers with true self-service to provision, recover, or clone databases from production and test environments—no matter where they are deployed.
“The goal of automation is to reduce the administrative burden on DBAs, so they can focus on higher-value tasks – things like database architecture and performance optimization – that have an impact on the customer experience or otherwise deliver real value to the business,” said Rojas.
Interest and investment is moving strongly in this direction. According to a February 2026 Forrester Total Economic Impact™ study of Nutanix Database Service (NDB), organizations adopting DevOps database automation platforms like NDB achieve a 136% ROI and $2.0 million net present value over three years. They report dramatic gains including reducing database provisioning time from weeks to minutes or hours (saving 10 hours of IT staff time and two weeks—or 80 hours—of developer wait time per request), more than 95% reduction in database patching time, dramatic compression of cloning/refresh and backup/restore cycles (60-80% faster), plus major efficiencies in managing hybrid multicloud database fleets.
AI has left an indelible impact on how databases are automated and the DevOps that ensures that everything runs smoothly. Agentic AI, in particular, has been transformative to every part of the database workflow and platform engineering has proven vital to keeping the train on its tracks.
Ultimately, AI and databases may form a self-reinforcing cycle of growth: as AI drives demand for more databases, organizations will increasingly rely on AI-powered automation to help manage and scale them.
“You need higher automation to deliver the quality of service that applications have become accustomed to or are demanding from these underlying systems,” said Mohindroo.
Paul Gillin is the former editor-in-chief of Computerworld and founding editor of TechTarget. He’s the author of five books about social media and online communities. Find him @pgillin.
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