How AI Agents Are Transforming Enterprise Data Work on Databricks
AI agents are moving fast from experiment to infrastructure. Gartner expects 40% of enterprise applications to embed task-specific agents by the end of 2026, up from under 5% in 2025.¹ The harder number sits just behind it: only around 31% of enterprises report an agent running in production, with banking and insurance well ahead of the pack.²
The gap between those two figures is rarely about the model. It’s about whether the data underneath the agent is current, governed, and connected to tools it’s allowed to use. Most agent projects stall there, not at the prompt.
Databricks was built to close that gap, and getting the first agent into production without spending the next two quarters rebuilding data plumbing first is achievable.
Why the Production Gap Exists
An agent that reasons well on a demo dataset can still fail in production if the data it touches is stale, duplicated, or scattered across systems with no shared definition of “customer” or “revenue.” Add governance requirements and the need to evaluate output before it reaches a user, and it’s easy to see why so many pilots never graduate.
That’s not a reason to slow down. It’s a reason to build the agent on a platform where data, governance, and evaluation already live together, instead of assembling them project by project.
What a Production-Grade Agent Needs
Reasoning ability gets most of the attention, but production agents live or die on the layer underneath it:
- Reusable templates so teams aren’t rebuilding the same scaffolding for every use case.
- Grounded retrieval: direct, governed access to data instead of a static export.
- Model flexibility: a choice of OpenAI, Anthropic, Gemini, or open models like Llama, without a new platform contract.
- Built-in evaluation: Tracing turns “does this agent work” into something measurable before go-live.
- Governed tool access: control which tools an agent can call, with every call logged.
How to close the gap and build what is needed…
This is what Databricks and partner’s like us do best: First is identifying the actual business opportunity and outcome to drive, ultimately being able to answer why you are investing in any technology solution is paramount. You obviously want some sort of net benefit i.e. cost savings, productivity gains, revenue generation. The technology is cool but is it cool enough to spend millions of dollars not knowing on the front end what you can achieve with it?
Databricks brings the technologies like Agent Bricks and Mosaic AI that sit on top of Unity Catalog and the Lakehouse, so an agent’s data access, evaluation, and monitoring are governed the same way the rest of your platform already is. We bring decades of expertise managing complex business processes, change management, and of course, the technical execution capabilities to help you scale and realize the value and deliver the business outcomes you identified in your very first step.
What comes next after you define your clear goal, is mapping architecture and priorities before any building starts. From there, it’s all about quick and dirty business wins. That means proof of value and proof of concept exercises. But what happens when your internal teams are bogged down or you don’t have the expertise? You need a network of partners that you can align with that understand your business and who have executed these types of projects with speed and quality.
MVP Requirements for Proof of Value
| What It Means |
Use Case Statement | What is the business process/challenge/opportunity? Who does it impact/touch, is everyone in alignment? Why is it important and why now? What is the definition of success & how is it measured today? |
Business Value Hypothesis | Quantifiable and qualitative measure of what the potential net impact would be by leveraging agentic AI as a solution to automate, augment or improve the people, processes, and technology that is supporting the business opportunity currently. |
PoV Reference Architecture | The underlying technical components that will be required to validate the business value hypothesis. |
Change Management Plan | Preparing people and processes through a programmatic approach that lowers resistance barriers, improves communication and promotes stronger adoption & yields faster time to value, doing this in the proof of value sets expectations early so it’s not afterthought when you go to roll this out to the organization. |
Execution & Review | The actual development and testing of the solution: running in your environment against real data, not a sandboxed demo, measured against your KPI’s with a plan to operationalize. |
The Infinitive Approach
Our Guided Activation for Agents has three modes of delivery depending on how much you want to own: an advisory track where your team builds and we review, a managed build where we own delivery end to end, and ongoing embedded support once the agent is live. Every Guided Activation is built to prove value fast, and in most cases a proof of value can be delivered in as little as four weeks.
Mode | What It Means |
Advisory | Your team builds; Infinitive architects, reviews, and guides. |
Managed Delivery | Infinitive owns the build for the fastest route to a live agent. |
Operational Support | Infinitive stays on as extended team after launch. |
About Infinitive
Infinitive has spent more than two decades helping regulated, data-intensive industries, financial services, higher education, and media, turn platform investments into working systems. We’re a Databricks Silver Brickbuilder partner with specializations in Data Warehousing and Data Security & Governance, and our consultants carry Databricks and AWS certifications across data engineering, machine learning, and AI.
Every engagement is scoped and priced before we start, with an outcome defined up front rather than a running clock.
A Reasonable Next Step
If you have a use case in mind but aren’t sure whether your data is actually ready to support an agent in production, that’s a fifteen-minute conversation, not a commitment.
Talk to Infinitive about scoping a first production agent, and get a straight answer on what a four-week build would take for your environment.
References
- Gartner, cited in “Enterprise AI Agents Are Entering Production,” Forbes, April 2026.
- McKinsey & Company and S&P Global Market Intelligence, enterprise AI agent production adoption research, 2026.