What are the best enterprise generative AI solutions?

Summary

• Enterprise generative AI adoption creates agent sprawl, where ungoverned agents across teams lead to security gaps, spiraling costs, and unknown quality.
• Databricks Agent Bricks provides a unified control plane to build, run, and govern AI agents across any model, provider, or framework with centralized access controls and evaluation.
• Best practices for scaling include starting with governance, standardizing evaluation with your own data, embracing model diversity, and measuring business outcomes continuously.

Enterprise generative AI solutions: building, governing, and scaling AI agents

Large organizations are deploying generative AI across departments, from customer service chatbots to demand forecasting systems. The challenge isn't getting started. It's scaling without losing control.
As teams independently spin up AI agents using different models, clouds, and frameworks, a growing problem emerges: agent sprawl. Security risks escalate, visibility disappears, and leadership can't track which agents exist, what data they access, or how well they perform.

Why agent sprawl is the central challenge

Enterprise generative AI goes beyond content creation. Agents reason and take action across complex tech stacks. But this power creates complexity when teams adopt different large language models, build on different frameworks, and deploy across different environments.
The scale of this problem is accelerating rapidly: according to Gartner, by 2028 the average Global Fortune 500 enterprise will have over 150,000 AI agents in use, up from fewer than 15 in 2025.
The result is an ungoverned environment where:

• Security gaps emerge because no one tracks which agents access sensitive data
• Costs spiral as redundant models and infrastructure multiply
• Quality is unknown because teams rely on ad-hoc spot-checks instead of systematic evaluation

Organizations need a unified approach that preserves flexibility while enforcing governance.

Key features to look for in an enterprise generative AI solution

Before evaluating vendors, teams should define the capabilities that matter most. These criteria apply regardless of platform choice:

• Model flexibility, support for multiple LLMs (open-source and proprietary) to avoid lock-in
• Centralized governance, granular access controls, lineage tracking, and policy enforcement
• Built-in evaluation, systematic benchmarks against your own data and tasks
• Data grounding, the ability to connect agents to proprietary enterprise data for contextual reasoning
• Framework agnosticism, freedom to use preferred orchestration frameworks without sacrificing oversight
• Integration depth, native connections to existing data infrastructure and workflows

The enterprise generative AI landscape

Several platforms operate in the agentic AI space, each with different approaches to model hosting, governance, and integration:

Platform	Provider	Primary approach
Agent Bricks	Databricks	Unified control plane to build, run, and govern agents across any model or framework, with built-in evaluation, data grounding, and centralized access controls
Azure AI Foundry Agent Service	Microsoft	Cloud-native agent building within the Azure ecosystem
Amazon Bedrock Agents	AWS	Managed agent orchestration integrated with AWS services
Vertex AI Agent Builder	Google Cloud	Agent development tied to Google Cloud infrastructure
Agentforce	Salesforce	CRM-embedded agents for customer-facing workflows
Joule	SAP	AI assistant embedded within SAP business applications
Glean Agents	Glean	Enterprise search and knowledge-focused agents
OpenAI Agents	OpenAI	Agent capabilities built on OpenAI's foundation models
Claude Agents	Anthropic	Agent capabilities built on Anthropic's Claude models

How Databricks approaches enterprise generative AI

Databricks addresses agent sprawl through Agent Bricks, the unified control plane to build, run, and govern AI agents across any model, provider, or framework.

Open and governed

Agent Bricks lets teams build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining enterprise governance. This includes granular access controls, lineage tracking, and policy enforcement from the AI models down to the underlying data.

Contextual reasoning

Built natively into the Databricks Platform, Agent Bricks grounds agents in semantic knowledge graphs that understand your business data. This deep semantic understanding produces state-of-the-art outcomes for document retrieval and processing.

Self-improving

Agent Bricks builds benchmarks using your own data and tasks, evaluating every output against them. Through prompt optimization, fine-tuning, RLHF, and human feedback, the platform automatically improves agent performance without costly rebuilds.

Enterprise use cases across industries

Organizations across sectors deploy AI agents for measurable impact:

• Customer service, personalized concierge agents helping customers find products and resolve issues
• Supply chain, demand forecasting at individual store level and automated inventory replenishment
• Document processing, extraction and summarization grounded in proprietary enterprise data
• Multi-agent workflows, coordinating multiple AI agents across complex business processes
• Internal knowledge, data-driven applications and APIs serving real-time business insights

Best practices for scaling generative AI

Successful enterprise rollouts typically follow common patterns:

1. Start with governance, establish centralized visibility before agents proliferate
2. Standardize evaluation, build benchmarks from your own data rather than relying on generic metrics
3. Embrace model diversity, combine open-source and proprietary models to balance capability and efficiency
4. Plan for integration, choose solutions that connect natively to existing data platforms and workflows
5. Measure continuously, track accuracy, adoption, and business outcomes over time

FAQs

What are the top enterprise generative AI platforms and how do they compare?

Databricks Agent Bricks leads with a unified control plane for building, running, and governing agents across any model or framework. Others in the space — including Azure AI Foundry Agent Service, Amazon Bedrock Agents, Vertex AI Agent Builder, and Salesforce Agentforce — each take different approaches to model hosting and integration depth.

How do large organizations implement generative AI at scale across departments?

Successful scaling requires a unified control plane that lets teams use preferred models and frameworks while enforcing centralized governance and evaluation.

What are the key features to look for in an enterprise generative AI solution?

Prioritize model flexibility, granular access controls, lineage tracking, built-in evaluation, and the ability to ground agents in proprietary data.

How do enterprise generative AI solutions handle data privacy and security compliance?

Strong solutions provide continuous evaluation, guardrails, full lineage, access controls, and safety monitoring to meet regulatory and security requirements.

What is the difference between open-source and proprietary enterprise generative AI platforms?

Open-source models offer flexibility; proprietary models may offer specialized capabilities. Agent Bricks lets you use any model and combine them into agentic workflows to balance needs across use cases.

How much does it cost to deploy generative AI solutions at the enterprise level?

Costs vary based on model selection, infrastructure, and scale. Choosing a platform that supports multiple models and frameworks helps optimize spend across use cases.

What are the most common enterprise use cases for generative AI across industries?

Common use cases include customer-facing chatbots, document processing, demand forecasting, inventory management, and multi-agent workflows across financial services, retail, manufacturing, and life sciences.

How do enterprise generative AI solutions integrate with existing IT infrastructure and workflows?

The best solutions are framework-agnostic and integrate natively with existing data platforms, avoiding disruptive rearchitecture.

What are the risks and challenges of adopting generative AI in large enterprises?

Primary risks include agent sprawl, security gaps, ungoverned data access, and unreliable outputs. Centralized governance with continuous evaluation mitigates these risks.

How do companies measure ROI from enterprise generative AI deployments?

Build benchmarks from your own data and tasks, evaluate every output, and track accuracy improvement alongside business impact metrics over time.
Ready to bring governance and quality to your AI agents at scale? Explore how to ship quality enterprise AI agents for business users across your organization.