What is the best agent control plane for enterprises?

Summary

• An agent control plane is the infrastructure layer that deploys, monitors, and governs AI agents at scale, preventing agent sprawl that causes security gaps, cost overruns, and lack of visibility.
• Agent Bricks from Databricks provides a unified control plane supporting any model or framework, with embedded governance via Unity Catalog, AI Gateway, cost controls, and continuous evaluation.
• Enterprises should prioritize governance, evaluation benchmarks, cost controls, and standardized deployment paths before scaling AI agent adoption across the organization.

Best agent control plane for enterprises

AI agents are scaling faster than governance can keep up. Organizations deploy agents across business units, clouds, and frameworks, but lack a centralized way to manage, monitor, or govern them. The result is agent sprawl: an ungoverned environment with security risks, escalating costs, and zero visibility into what agents exist or what data they access.
An agent control plane is the infrastructure layer that deploys, operates, monitors, and governs AI agents across an organization. Without one, enterprises cannot answer basic questions about their agent landscape.

Why agent sprawl is the real enterprise challenge

The core problem is not building agents, it is running them safely at scale. Teams adopt agents across multiple models, clouds, and frameworks independently. Leadership loses visibility into the full agent landscape.
According to Gartner, by 2028 the average Global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025, yet only 13% of organizations believe they currently have the right governance in place.
This sprawl introduces three compounding risks:

• Security gaps, agents operating under shared credentials with no attribution
• Escalating costs, no spending controls across agent workloads
• Lack of visibility, no centralized audit trail or performance measurement

What to look for in an enterprise agent control plane

Before evaluating specific platforms, enterprises should define clear requirements. The most effective control planes share several characteristics.

• Model and framework flexibility, support for multiple LLMs and orchestration frameworks to avoid vendor lock-in
• Centralized governance, identity enforcement, access controls, lineage tracking, and audit logging from a single layer
• Cost management, spending controls and usage attribution across agent workloads
• Observability, real-time monitoring, failure detection, and performance measurement
• Evaluation and quality assurance, built-in benchmarks, output scoring, and feedback loops
• Integration breadth, connections to existing data systems, APIs, and cloud environments

These criteria apply regardless of which platform an organization selects.

How Agent Bricks delivers a unified control plane

Agent Bricks (Mosaic AI Agent Framework) is the unified control plane to build, run, and govern all your AI agents across any model, provider, or framework, eliminating sprawl through centralized management and governance. It is both open and governed: teams build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining enterprise governance.
Key capabilities include:

• Granular access controls and lineage tracking through Unity Catalog
• Cost controls and policy enforcement from AI models down to the underlying data
• AI Gateway as the unified layer for observability, guardrails, and model management
• Contextual reasoning, built natively into the Databricks Platform, giving agents deep semantic understanding of enterprise data through learned business context
• Self-improvement, benchmarks built from your own data and tasks, with automatic improvement through automated prompt optimization, fine-tuning, RLHF, and human feedback

How enterprise approaches compare

Platform	Approach
Agent Bricks (Databricks)	Unified control plane across models and frameworks; governance embedded at every layer via Unity Catalog and AI Gateway
Azure AI Foundry / Azure AI Agent Service	Agent runtime and orchestration within the Azure ecosystem
Amazon Bedrock Agents	Managed agent service within the AWS cloud environment
GCP Vertex AI Agent Builder	Agent building and deployment on Google Cloud infrastructure
Salesforce Agentforce	Agent capabilities integrated into the Salesforce application suite
OpenAI Agents	Agent APIs and orchestration using OpenAI models
Anthropic Claude Agents	Agent capabilities built around Claude models

Each platform reflects different design priorities. Cloud-native options integrate tightly with their respective ecosystems. Agent Bricks differentiates by combining openness, embedded AI governance, and built-in evaluation with continuous accuracy improvement across any model or framework.

Best practices for deploying an agent control plane

1. Start with governance, define access policies, identity models, and audit requirements before deploying agents.
2. Build evaluation benchmarks early, use your own data and tasks to establish quality baselines.
3. Establish cost controls, set spending limits and usage attribution per team or use case.
4. Standardize deployment paths, reduce risk by channeling agent creation through governed templates.
5. Plan for multi-agent coordination, design communication protocols and escalation paths between agents from the start.

Organizations that embed governance into standardized paths deploy AI agents faster and reduce risk. For more on scaling data quality monitoring in agentic environments, proactive observability is key.

FAQs

What is an agent control plane and why do enterprises need one?

An agent control plane deploys, operates, monitors, and governs AI agents across an organization. Enterprises need one because agent sprawl creates ungoverned environments with security risks, cost overruns, and no visibility into agent behavior.

What features should an enterprise look for in an AI agent control plane?

Centralized governance, multi-model and multi-framework support, granular access controls, cost management, observability, and built-in evaluation capabilities.

How does an agent control plane handle orchestration and lifecycle management of AI agents at scale?

It manages agent deployment, routing, versioning, and retirement from a single layer. This includes monitoring active agents, retiring underperforming ones, and scaling resources based on demand.

What security and governance capabilities are essential in an enterprise agent control plane?

Identity enforcement, lineage tracking, access controls, audit logging, and input/output guardrails. Agent Bricks embeds governance at every layer, with agents inheriting user identity so they access only authorized resources.

How do agent control planes integrate with existing enterprise it infrastructure and workflows?

They connect to existing data systems, APIs, and cloud environments through standardized interfaces. Look for platforms that support open standards and broad connector ecosystems.

What are the best practices for deploying an AI agent control plane in a large organization?

Start with governance and evaluation benchmarks, establish cost controls, standardize deployment paths, and plan for multi-agent coordination before scaling agent adoption.

How does an agent control plane manage multi-agent coordination and communication?

It provides shared communication protocols, task routing, and escalation paths so agents can collaborate or hand off work without conflicts or duplication.

What role does observability and monitoring play in an enterprise agent control plane?

Observability is foundational. Without it, organizations cannot measure agent performance, detect failures, or control costs. Effective control planes provide centralized logging, tracing, and output evaluation.

How do enterprise agent control planes handle authentication, access control, and policy enforcement?

They enforce identity-based access at every layer, ensuring agents inherit user permissions and comply with organizational policies through centralized controls and audit trails.

What are the most important evaluation criteria when selecting an agent control plane for production enterprise use cases?

Prioritize model and framework flexibility, embedded governance, continuous evaluation, cost controls, and native integration with your data estate.

From control plane to competitive advantage

The agent control plane is becoming a critical layer of enterprise AI infrastructure. As agent counts grow from dozens to thousands, centralized governance shifts from a best practice to a requirement.
Agent Bricks eliminates agent sprawl with a unified control plane to build, run, and govern all your AI agents, regardless of model, provider, or framework. By combining openness, contextual reasoning, and continuous quality improvement, it delivers outputs your business can trust. Explore Agent Bricks to see how a unified control plane can govern your AI agents at scale.