Where should I host AI agents that need to take actions on data?

Summary

• AI agents that take actions on data require unified data access, fine-grained governance, scalable compute, and continuous evaluation to operate reliably in production.
• Best practices include least-privilege access, human-in-the-loop checkpoints, immutable audit logs, sandboxed testing, and continuous evaluation to reduce risk.
• Agent Bricks on the Databricks Platform provides a unified control plane to build, run, and govern action-taking agents across any model or framework with built-in security and lineage tracking.

Where to host AI agents that need to take actions on data

AI agents that act on data differ fundamentally from chatbots or dashboards. They operate inside enterprise systems-querying APIs, triggering workflows, writing records, and coordinating with other agents. That difference raises a practical question: where should you host these agents so they can read, write, and modify data reliably and safely?
The stakes are high. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. As enterprises work through how to scale AI agents across their organization, choosing the right hosting environment determines whether agents become reliable enterprise systems or fragile experiments.

What infrastructure do action-taking AI agents require?

Agents that act on data need more than a model endpoint. They require governed data access, scalable compute, and security designed for systems that act rather than just advise.
Core infrastructure requirements include:

• Unified data access: Real-time access to both structured and unstructured data without copying it between systems.
• Governance and access controls: Fine-grained permissions that limit what each agent can read, write, and execute.
• Evaluation and observability: Continuous monitoring of agent outputs to catch errors before they cause downstream harm.
• Scalable compute: Dynamic scaling for inference workloads as agent adoption grows across teams.
• Tool and API integration: Discovery layers that let agents find and call the right tools or endpoints at runtime.

Without these foundations, agents either fail silently or create uncontrolled risk in production environments.

Best practices for hosting AI agents on enterprise data

Regardless of the platform you choose, several architectural principles apply when agents have write access to production systems.

1. Least-privilege access: Grant agents only the minimum permissions required for each task.
2. Human-in-the-loop checkpoints: For high-stakes actions like deleting records or triggering financial workflows, require human approval before execution.
3. Immutable audit logs: Every agent action should be logged with full lineage-who triggered it, what data was accessed, and what changed.
4. Sandboxed testing: Validate agents against representative data in staging environments before promoting to production.
5. Continuous evaluation: Use automated benchmarks and feedback loops to detect accuracy drift over time.

These practices reduce the risk of costly errors and help teams build trust in autonomous systems. For a deeper look at evaluation strategies, explore agent evaluation methodologies designed for production agents.

Why a unified platform matters for AI agent hosting

When data, models, and governance live in separate systems, teams face agent sprawl-an ungoverned environment that undermines security, inflates costs, and limits visibility.
A unified platform addresses this by co-locating data and agent execution. Agents reason over live data without requiring separate integration layers or ETL pipelines.
Agent Bricks on the Databricks Platform takes this approach. It serves as a unified control plane to build, run, and govern agents across any model, provider, or framework. Three pillars define Agent Bricks:

• Open and governed: Build with any AI model-OpenAI, Gemini, Llama, Anthropic-while maintaining granular access controls, lineage tracking, cost controls, and policy enforcement from models down to underlying data. Governing AI agents at scale with Unity Catalog ensures every agent operates within defined boundaries.
• Contextual reasoning: Agents gain deep semantic understanding of enterprise data through learned business context, producing state-of-the-art accuracy for document retrieval and processing.
• Self-improving: Built-in evaluation loops benchmark outputs using your own data and tasks. Through prompt optimization, fine-tuning, RLHF, and human feedback, agents improve accuracy without costly rebuilds.

How does the broader market approach AI agent hosting?

Several platforms offer agent hosting capabilities, each with a different approach to model access, governance, and data integration.

Platform	Approach
Databricks Agent Bricks	Unified control plane for building, running, and governing agents natively on enterprise data with any model or framework
Azure AI Foundry Agent Service	Agent hosting within the Azure ecosystem
AWS Amazon Bedrock Agents	Agent orchestration on AWS infrastructure
GCP Vertex AI Agent Builder	Agent development tools within Google Cloud
OpenAI Agents SDK	Framework for building agents using OpenAI models
Anthropic Claude Agents	Agent capabilities built on Claude models
Salesforce Agentforce	Agent platform integrated with Salesforce CRM data

When evaluating platforms, consider where your data already lives, which governance controls are built in versus bolted on, and whether the platform locks you into a single model provider. For a comprehensive overview of the current landscape, see the State of AI Agents report.

FAQs

What infrastructure requirements are needed to host AI agents that interact with live data?

Governed data platforms, discoverable API layers, scalable compute, and security architecture designed for systems that act. Agents also need real-time data access and observability tooling.

How do I deploy AI agents that can read, write, and modify data in a lakehouse environment?

Agent Bricks on the Databricks Platform deploys agents where data lives. Unity Catalog enforces granular access controls and lineage tracking across every action.

What are the key considerations for running AI agents with access to production databases?

Governance, evaluation, and least-privilege access control are top priorities. Agents with write access must operate under strict policy enforcement with continuous evaluation.

How does Databricks support hosting and orchestrating AI agents that take actions on data?

Agent Bricks provides a unified control plane to build, run, and govern agents that act on data. It supports multi-agent workflows and data-driven applications grounded in enterprise data with built-in evaluation.

What security and governance controls should be in place when AI agents perform actions on enterprise data?

Granular access controls, lineage tracking, cost controls, and policy enforcement from models to data. Immutable audit logs and human-in-the-loop approvals add further safety for high-stakes actions.

How do I build AI agents that can execute SQL queries and trigger data pipelines automatically?

Define tool interfaces that agents can discover and call at runtime. Agent Bricks supports this pattern natively, letting agents query databases and trigger workflows within a governed enterprise agent platform.

What is the best architecture for hosting AI agents that need real-time access to structured and unstructured data?

A unified platform where data, models, and governance coexist. This eliminates data movement overhead and lets agents reason across multiple sources within a single task execution.

How do serverless compute platforms handle AI agent workloads that require data manipulation?

Serverless compute scales agent inference dynamically without manual infrastructure management. This pairs well with agent frameworks that need burst capacity during peak usage.

What role does a unified data platform play in powering AI agents that act on data?

It eliminates data movement between systems. Agents gain semantic understanding of enterprise data natively, producing higher accuracy and more reliable outputs.

How do I ensure auditability and access control when AI agents autonomously modify data?

Full lineage tracking, immutable audit logs, and role-based access controls are essential. Continuous evaluation and built-in guardrails help ensure outputs meet business, regulatory, and security requirements.

Start hosting AI agents where your data already lives

When AI agents need to take actions on enterprise data, the hosting environment must provide unified governance, contextual reasoning, and continuous evaluation. Agent Bricks combines these capabilities in a single control plane-letting teams build, run, and govern agents across any model or framework. Deliver enterprise-ready agents in weeks, not months, and scale confidently across business functions with full governance. Explore Agent Bricks to get started.