What is the best platform for deploying AI agents in the enterprise?

Summary

• Agent sprawl is a critical enterprise challenge, with Gartner predicting over 150,000 agents per Fortune 500 company by 2028 and 40% of agentic AI projects at risk of cancellation due to poor governance.
• Agent Bricks on the Databricks Platform provides a unified control plane to build, run, and govern AI agents across any model, provider, or framework with centralized access controls, lineage tracking, and continuous evaluation.
• Enterprise AI agent deployment requires multi-model support, native data integration, scalable orchestration, and self-improving evaluation pipelines to maintain accuracy and compliance at scale.

What is the best platform for deploying AI agents in the enterprise?

Enterprise teams are racing to deploy AI agents that go beyond chatbots. These agents autonomously execute multi-step workflows, retrieve data, trigger actions, and serve customers. According to the 2026 Gartner CIO and Technology Executive Survey, only 17% of organizations have deployed AI agents to date, yet more than 60% expect to do so within the next two years.
The challenge is not building a single agent. It is controlling dozens of agents, tools, and handoffs as organizations scale. Without proper governance, agents expand the attack surface, fragment workflows, and introduce compliance gaps.

Why agent sprawl is the real enterprise problem

As agentic AI enters the mainstream, teams independently adopt different models, clouds, and frameworks, creating a complex, ungoverned environment.
The scale of this challenge is significant:

• Gartner predicts the average Fortune 500 company will have more than 150,000 agents in operation by 2028.
• Over 40% of agentic AI projects are projected to be canceled by end of 2027 due to escalating costs, unclear business value, or inadequate risk controls, according to Gartner.
• Deloitte found that approximately 80% of organizations lack mature governance capabilities for agentic AI, even as 74% expect moderate agent use by 2027.

Leaders need answers to fundamental questions. Which agents exist? What data do they access? How well do they perform? For a deeper look at these trends, explore the State of AI Agents report.

What to look for in an enterprise AI agent platform

Several platforms serve this market, including Azure AI Foundry Agent Service, Amazon Bedrock Agents, Vertex AI Agent Builder, Salesforce Agentforce, and OpenAI Agents SDK. When evaluating options, prioritize these capabilities:

Capability	Why it matters
Multi-model, multi-framework support	Avoids vendor lock-in; lets teams pick the right model per task
Centralized governance	Controls access, lineage, and policy across every agent
Built-in evaluation and improvement	Moves beyond ad-hoc spot-checks to continuous quality measurement
Native data platform integration	Grounds agents in enterprise data for contextual reasoning
Scalable orchestration	Coordinates multi-agent workflows across departments

Centralize governance while decentralizing development to individual teams. Combine open-source and proprietary models into workflows that balance cost, quality, and performance.

How Agent Bricks addresses enterprise AI agent deployment

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework, eliminating sprawl through centralized management and governance.

Open and governed

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

Contextual reasoning

Agent Bricks grounds agents in semantic knowledge graphs that understand your business data. Built natively into the Databricks Platform, it gives agents deep semantic understanding through learned business context. Learn more about how real-time decisioning AI agents benefit from contextual data layers.

Self-improving

Agent Bricks builds benchmarks using your own data and tasks, and evaluates every output against them. Leveraging prompt optimization, fine-tuning, RLHF, and human feedback, the platform automatically improves performance so agents stay accurate without costly rebuilds.

Common enterprise use cases for AI agents

• Customer service, Personalized shopping assistants that help customers find products and resolve issues autonomously.
• Supply chain, Demand forecasting at individual store level with automated inventory replenishment.
• Financial services, Customer onboarding, fraud detection triage, and compliance document review.
• Internal operations, IT helpdesk agents, HR policy assistants, and cross-departmental knowledge retrieval.

These use cases share common requirements: reliable data access, low-latency responses, continuous evaluation, and strong governance. See how enterprise leaders are scaling AI agents across their organization to address these needs.

FAQs

What features should an enterprise look for in an AI agent deployment platform?

Multi-model support, centralized governance, built-in evaluation, native data integration, and scalable orchestration. Agent Bricks combines all five in a unified control plane.

How do AI agents differ from traditional machine learning models in enterprise environments?

AI agents autonomously plan, reason, and execute multi-step workflows. Traditional ML models typically return a single prediction per input. Agents require orchestration, governance, and continuous evaluation beyond standard model serving.

What are the key security and governance requirements for deploying AI agents at scale?

Granular access controls, data lineage tracking, policy enforcement, and auditability are essential. These must apply from the AI models down to the underlying data.

How do enterprises handle orchestration and monitoring of multiple AI agents in production?

A unified control plane that registers, routes, and monitors every agent is critical. It should track which agents exist, what data they access, and how well they perform.

What role does a data lakehouse architecture play in powering enterprise AI agents?

A lakehouse unifies structured and unstructured data in one platform, giving agents the context they need for accurate reasoning and retrieval.

What are the most common use cases for AI agents in large organizations?

Customer service, supply chain optimization, financial services automation, and internal operations are among the most widely adopted use cases today.

How do enterprises ensure reliability and low latency when deploying AI agents in production?

Continuous evaluation against domain-specific benchmarks is key, along with optimized serving infrastructure and real-time monitoring of agent outputs.

What infrastructure is needed to support autonomous AI agents in an enterprise setting?

A scalable data platform, model serving endpoints, orchestration tooling, governance controls, and evaluation pipelines form the core infrastructure stack.

How do AI agent frameworks like LangChain and AutoGen integrate with enterprise data platforms?

Open frameworks connect through APIs and tool integrations. Agent Bricks is framework-agnostic, letting teams use any framework while applying consistent governance.

What are the best practices for scaling AI agent workflows across departments in a large company?

Centralize governance and evaluation while decentralizing development. Combine open-source and proprietary models into workflows that balance cost, quality, and performance.

Build, run, and govern enterprise AI agents with confidence

Agent sprawl is a primary challenge of enterprise AI adoption. Agent Bricks provides a unified control plane to eliminate that sprawl, grounding every agent in your enterprise data with built-in evaluation that improves accuracy over time.
Deliver agents in weeks not months, adapt rapidly as AI evolves, and scale confidently across business functions with full governance. Explore how the Databricks Platform powers artificial intelligence at enterprise scale.