How do you build agentic applications with access to company data?

Summary

• Building agentic applications over enterprise data requires solving three challenges: grounding agents in business context, governing data access, and continuously measuring output quality.
• Key architecture patterns include retrieval-augmented generation (RAG), tool use with function calling, and multi-step orchestration with shared state management.
• Databricks Agent Bricks provides a unified control plane to build, govern, and improve AI agents across any model or framework using Unity Catalog, semantic knowledge graphs, and automated evaluation.

How to build agentic applications with access to company data

Most enterprises want AI agents that can reason over internal data, not just answer generic questions. The challenge is connecting large language models to proprietary databases, documents, and knowledge bases while maintaining security, governance, and accuracy. As organizations explore the state of AI agents, getting this right means solving three problems at once: grounding agents in your actual business data, governing what they can access, and continuously measuring whether they produce trustworthy results.

Why enterprise AI agents need more than a model

An agentic application goes beyond a simple chatbot. It can plan multi-step tasks, call tools and APIs, retrieve relevant documents, and take actions on behalf of users. That makes enterprise agentic AI a matter of data governance, observability, and architecture-not just a model choice.
The scale of this shift is significant. According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. That rapid adoption makes it critical to get the foundations right now.
Three core challenges emerge as organizations scale AI agents across their organization:

• Agent sprawl. Teams adopt different models, clouds, and frameworks without centralized oversight. Leaders can't answer "Which agents exist?", "What data do they access?", or "How well do they work?"
• Shallow reasoning on enterprise data. Agents lack business context and semantics, causing them to retrieve incorrect information or misuse tools.
• No systematic quality measurement. Without continuous evaluation, incorrect responses go undetected.

Key architecture patterns for agentic applications

Before selecting any platform, teams should understand the foundational patterns that make agentic applications work over enterprise data.

Retrieval-augmented generation (RAG)

RAG retrieves relevant documents from a knowledge base and feeds them to an LLM at inference time. This grounds responses in real enterprise data rather than relying solely on training knowledge.

• Store document embeddings in a vector database for semantic search.
• Chunk documents into retrievable units sized for your LLM's context window.
• Apply metadata filtering to scope retrieval to the right data domain.

Tool use and function calling

Agents become powerful when they can invoke external tools-database queries, API calls, calculations-during their reasoning loop.

• Define each tool with a clear schema describing inputs, outputs, and purpose.
• Let the LLM select and sequence tools based on the user's request.
• Validate tool outputs before passing them back into the reasoning chain.

Multi-step orchestration

Complex enterprise tasks require agents that decompose problems into discrete steps.

• Assign specialized sub-agents or tools to each step.
• Use an orchestration layer to manage execution order and error handling.
• Maintain shared memory or state across steps for coherent reasoning.

Governing agent access to enterprise data

Governance is not optional when agents touch sensitive company information. Treat every agent like an identity principal with scoped, auditable permissions. The AI Gateway serves as a critical governance layer for managing agent access at scale.

Practice	Description
Least-privilege access	Grant only the permissions each agent needs for its specific task
Time-bound credentials	Rotate and expire agent credentials on a defined schedule
Lineage tracking	Record which data sources each agent accessed and when
Policy enforcement	Apply compliance rules at the data, model, and output layers
Centralized audit	Log all agent actions in a single, queryable system

How Agent Bricks addresses these challenges

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

• Open and governed. Build with any AI model-OpenAI, Gemini, Llama, Anthropic-and any framework while maintaining enterprise governance. Unity Catalog provides granular access controls, lineage tracking, and policy enforcement from the model layer down to the underlying data.
• Contextual reasoning. Built natively into the Databricks Platform, Agent Bricks grounds agents in semantic knowledge graphs that represent your business data, producing high-accuracy document retrieval and processing.
• Self-improving. Agent Bricks builds benchmarks using your own data, evaluates every output with LLM Judges, and applies prompt optimization, fine-tuning, and human feedback (ALHF) to improve accuracy over time-without costly rebuilds.

A typical build process follows these steps:

1. Define the agent's task and connect enterprise data sources.
2. Ground the agent using RAG and semantic knowledge graphs.
3. Evaluate with task-specific benchmarks and grounded reasoning.
4. Deploy through Agent/Model Serving with AI Gateway for governed access.
5. Improve continuously using human feedback loops.

FAQs

What is an agentic application and how does it differ from a traditional AI chatbot?

An agentic application uses an LLM to plan, reason, and take multi-step actions autonomously. A traditional chatbot follows scripted or single-turn response patterns without tool use or dynamic reasoning.

How do you securely connect an AI agent to internal company databases?

Grant explicit, scoped permissions and enforce least-privilege access. Agent Bricks provides granular access controls and lineage tracking through Unity Catalog.

What is retrieval-augmented generation (RAG)?

RAG retrieves relevant documents from a knowledge base and feeds them to an LLM at inference time. This grounds responses in real enterprise data rather than relying solely on training knowledge.

How do you implement tool use and function calling in LLM-based agents?

Define tools as callable functions with clear schemas. The LLM invokes them during its reasoning loop based on the user's request and task requirements.

What are the best practices for governing AI agent access to sensitive data?

Apply identity and access management, enforce least-privilege and time-bound permissions, and audit all actions centrally. Agent Bricks addresses this through its unified control plane and Unity Catalog governance, with the AI Gateway as the governance layer for agentic AI.

How do you build a multi-step AI agent?

Decompose the workflow into discrete steps, assign tools and data sources to each, and use an orchestration layer to manage execution and state.

What role does a vector database play in agentic applications?

A vector database stores document embeddings for semantic search. This is the retrieval layer in RAG, enabling agents to find relevant information even without exact keyword matches.

How do you ensure data privacy and compliance?

Embed governance into the agent platform by design. Enforce policy at every layer-model access, data retrieval, and output-with lineage tracking and auditable access controls.

What frameworks and tools are available for building agentic AI applications?

Options include Databricks Agent Bricks, AWS Amazon Bedrock Agents, GCP Vertex AI Agent Builder, Azure AI Foundry Agent Service, and OpenAI Agents. Each offers different tradeoffs in openness, governance depth, and cloud integration.

How do you evaluate and monitor agentic applications in production?

Build task-specific benchmarks, run continuous evaluations, and incorporate human feedback. Agent Bricks automates this with LLM Judges and built-in evaluation suites generated from your data.

Put your enterprise data to work

Building agentic applications that reason over company data requires grounding in business context, governed access, and continuous quality measurement. Agent Bricks provides a unified control plane for all three-helping teams move from prototype to production with confidence on the Databricks Platform. Explore Agent Bricks to get started.