What is the best GenAI infrastructure for enterprise teams?

Summary

• Enterprise GenAI infrastructure must solve agent sprawl, contextual reasoning on proprietary data, and measurable quality improvement to succeed at scale.
• Agent Bricks on the Databricks Platform provides a unified control plane to build, run, govern, and evaluate AI agents with model flexibility and built-in benchmarking.
• Best practices for enterprise LLMOps include centralized governance, automated evaluation pipelines, human feedback loops, and alignment with regulatory frameworks from day one.

Best GenAI infrastructure for enterprise teams

Enterprise teams are deploying generative AI at scale, but most hit the same wall: fragmented tools, ungoverned models, and no reliable way to measure quality. The core problem is lack of control over model behavior.
The result is agent sprawl-different departments adopting different models, clouds, and frameworks with no central visibility. According to Gartner, the average Fortune 500 enterprise will have over 150,000 AI agents in use by 2028, up from fewer than 15 in 2025. That 10,000x increase makes unified governance an urgent priority.

What enterprise GenAI infrastructure must solve

Before selecting a platform, teams need to understand three core problems any enterprise GenAI stack must address.

• Governance over agent sprawl. Teams adopt AI agents across multiple models and frameworks, creating security risk and zero visibility into which agents exist, what data they access, and how well they perform.
• Contextual reasoning on enterprise data. Generic AI models lack business context. Without semantic understanding of proprietary data, agents produce unreliable outputs.
• Measurable, improving quality. Without systematic evaluation, incorrect responses go undetected until they cause damage.

Essential safeguards include continuous monitoring, human-in-the-loop review, explainability standards, access controls, data classification, and audit logging.

Key components of an enterprise GenAI stack

Any production-grade GenAI infrastructure should include these layers:

Layer	Purpose
Data foundation	Governed, unified data store (lakehouse, warehouse, or lake)
Model serving	Scalable inference for multiple model types
Agent orchestration	Workflow coordination across tools and APIs
Governance and security	Access controls, lineage, policy enforcement, audit trails
Evaluation and monitoring	Continuous quality measurement, benchmarking, feedback loops

A unified approach reduces integration burden and governance gaps.

How to evaluate enterprise GenAI platforms

When comparing platforms, prioritize these vendor-neutral criteria:

1. Model flexibility. Can you use models from multiple providers without lock-in?
2. Built-in evaluation. Does the platform offer systematic benchmarking against your own data and tasks?
3. Enterprise governance. Are access controls, lineage tracking, and policy enforcement native?
4. Contextual grounding. Can agents reason over your proprietary data with semantic understanding?
5. Operational maturity. Does the platform support production deployment, monitoring, and iterative improvement?

How Agent Bricks addresses enterprise requirements

Agent Bricks is the unified control plane to build, run, and govern AI agents-eliminating sprawl through centralized management. Learn more about how enterprise leaders are scaling AI agents across their organizations.

Open and governed

Build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining enterprise governance. This includes granular access controls, lineage tracking, cost controls, and policy enforcement from models down to data.

Contextual reasoning

Built natively into the Databricks Platform, Agent Bricks gives agents deep semantic understanding of enterprise data through learned business context. This produces state-of-the-art outcomes, including the highest accuracy scores for document retrieval and processing.

Self-improving

Agent Bricks builds benchmarks using your own data and tasks, then evaluates every output against them. Through prompt optimization, fine-tuning, RLHF, and human feedback, the platform automatically improves agent performance without costly rebuilds.
This combination lets enterprise teams deliver agents in weeks, not months, and adapt quickly as AI evolves-reusing a governed framework across departments.

Best practices for enterprise LLMOps

Regardless of platform choice, these practices reduce risk and accelerate value:

• Establish evaluation pipelines from day one. Automated benchmarking catches regressions before they reach users.
• Centralize governance. A single policy layer across all agents prevents shadow AI and compliance gaps.
• Use human feedback loops. Expert review improves accuracy over time and builds institutional trust.
• Balance cost through model choice. Combine open-source and proprietary models in agentic workflows to optimize cost, quality, and latency.
• Align with regulatory frameworks. Map controls to standards like the EU AI Act and NIST AI RMF early.

FAQs

What are the key components of a GenAI infrastructure stack for enterprise use cases?

A complete stack includes a data layer, model serving, agent orchestration, governance controls, and evaluation tooling. These layers work together to ensure agents are accurate, secure, and auditable.

How should enterprise teams evaluate GenAI platforms for scalability and security?

Look for centralized governance, granular access controls, lineage tracking, and the ability to scale across models and clouds without vendor lock-in.

What are the most important features to look for in an enterprise GenAI platform?

Model flexibility, built-in evaluation, enterprise governance, and contextual grounding in proprietary data. Agent Bricks provides all four through its unified control plane.

How do large enterprises deploy and manage large language models in production?

They use governed serving infrastructure with monitoring, guardrails, and evaluation pipelines. Agent Bricks offers unified deployment with AI Gateway to apply governance consistently across every model.

What are the data governance and compliance requirements for enterprise GenAI infrastructure?

Key requirements include access controls, audit trails, data lineage, and alignment with frameworks like the EU AI Act and NIST AI RMF. Governance must be continuous, not a one-time review.

How does Databricks support generative AI workflows for enterprise teams?

Agent Bricks provides the Mosaic AI Agent Framework, Agent Evaluation, and AI Gateway. Together, these let teams build, evaluate, deploy, and govern AI agents from a single control plane.

What role does a unified data lakehouse play in powering enterprise GenAI applications?

The lakehouse provides the governed data foundation agents need for contextual reasoning. It eliminates data copying across tools and keeps governance intact.

How can enterprise teams fine-tune foundation models on their own proprietary data securely?

Use a platform with built-in governance that keeps data in place. Agent Bricks supports fine-tuning and RLHF on your own data with enterprise access controls ensuring proprietary data stays governed.

What are the cost considerations when building GenAI infrastructure at enterprise scale?

Model choice, serving efficiency, and avoiding vendor lock-in are the biggest drivers. Combining open-source and proprietary models in agentic workflows helps balance cost, quality, and performance.

What best practices should enterprise teams follow for MLOps and LLMOps in generative AI projects?

Establish continuous evaluation, automated benchmarking, human feedback loops, and centralized governance from day one. These practices apply across any platform.

Bring governed GenAI agents to production

Enterprise GenAI infrastructure succeeds when building, running, governing, and evaluating agents happen in one place. Agent Bricks on the Databricks Platform gives enterprise teams the unified control plane to deploy AI agents grounded in their data, with the governance, model flexibility, and continuous improvement that production demands. Explore the state of AI agents to see how leading enterprises are putting these capabilities into action.