Detailed Guide to Using Databricks with Agentic AI

Databricks, with its unified Lakehouse Platform, offers a robust environment for developing, deploying, and managing Agentic AI systems. Agentic AI involves AI models (often Large Language Models – LLMs) that can reason, plan, use tools, and take autonomous actions. This guide will detail how to leverage Databricks for building such sophisticated AI agents.

Agentic AI systems go beyond simple prompt-response models. They require robust data infrastructure, flexible model serving, and sophisticated evaluation and monitoring capabilities. Databricks’ Lakehouse Platform provides these capabilities in a unified environment.

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I. Understanding the Core Components of an Agentic AI System on Databricks

Before diving into the “how,” let’s understand the key architectural components of an Agentic AI system and how Databricks supports them:

• The Agent (LLM as the Brain)

  This is typically a Large Language Model (LLM) that serves as the reasoning engine. It interprets user requests, plans actions, and decides which tools to use. Databricks supports integrating with various LLMs, including its own DBRX, open-source models, and commercial APIs (OpenAI, Anthropic, Google Gemini) via Mosaic AI Gateway. You can fine-tune custom LLMs on your proprietary data using Databricks’ ML capabilities.

• Tools

  Tools are functions or APIs that the agent can call to perform specific tasks. These extend the LLM’s capabilities beyond pure language generation, allowing it to interact with external systems, retrieve data, or execute code. Databricks’ Unity Catalog Functions are a first-class way to define and manage tools. These can be Python functions that interact with Delta tables, perform calculations, call external APIs (via Unity Catalog Connections), or execute code within a secure sandbox. Databricks also integrates well with popular agent frameworks like LangChain and LlamaIndex for defining tools.

• Memory

  Agents need memory to maintain context across multiple turns in a conversation or sequence of actions. This can be short-term (in-context learning) or long-term (retrieval-augmented generation – RAG). For structured and semi-structured long-term memory, Delta Lake tables are ideal, providing reliable, scalable storage. For RAG, Mosaic AI Vector Search is crucial, allowing you to store high-dimensional embeddings of your unstructured data and retrieve relevant chunks based on semantic similarity to user queries. Learn more about how to create and query a vector search index.

• Orchestration/Planning

  This is the logic that guides the agent’s decision-making process, including sequential tool use, conditional logic, and error handling. Frameworks like LangChain are commonly used here. Databricks Notebooks and Workflows provide a flexible environment to implement and execute these orchestration logics. The Mosaic AI Agent Framework provides specific tools and best practices for authoring, evaluating, and deploying agents. See the Get started with AI agents tutorial.

• Data (Foundation)

  High-quality, well-governed data is the bedrock for any effective AI agent. This includes data for training/fine-tuning LLMs, populating knowledge bases for RAG, and monitoring agent performance. The Lakehouse Platform unifies data warehousing and data lakes, making it easy to store, process, and govern all types of data using Delta Lake and Unity Catalog.

• Evaluation and Monitoring

  Critical for ensuring the agent’s performance, safety, and reliability in production. This involves tracking metrics, debugging errors, and continuously improving the agent. MLflow is integrated for experiment tracking, model registry, and logging agent runs. Mosaic AI Agent Evaluation provides specialized tools for evaluating agent quality, including LLM-as-a-judge, rule-based checks, and human feedback loops. Check out the Mosaic AI Agent Evaluation tutorial notebook.

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II. Steps to Build and Deploy Agentic AI on Databricks

Here’s a step-by-step guide to building and deploying Agentic AI on Databricks:

1. Step 1: Data Preparation and Ingestion (The Lakehouse Foundation)

   • Ingest Data: Use Databricks capabilities (Auto Loader, Delta Live Tables) to ingest data from various sources (databases, APIs, streaming data, documents, PDFs) into Delta Lake. You can find a tutorial for building an ETL pipeline with DLT here.
   • Clean and Transform: Utilize Spark and SQL on Databricks to clean, transform, and normalize your data. For unstructured data, process it into chunks suitable for RAG.
   • Govern Data with Unity Catalog: Implement Unity Catalog for centralized data governance, access control, and data lineage tracking. This is critical for ensuring the agent only accesses authorized and relevant data. Learn how to set up and manage Unity Catalog.

2. Step 2: Building Agent Tools

   • Identify Needed Actions: Determine what external systems or specific data queries your agent will need to perform.
   • Create Unity Catalog Functions: For most common scenarios, define your tools as Python functions registered in Unity Catalog. See user-defined functions (UDFs) in Unity Catalog for more details.
     • Structured Data Retrieval: Create functions that query specific Delta tables for structured information (e.g., customer account details, transaction history).
     • Unstructured Data Retrieval (RAG):
       • Embeddings: Use Databricks ML capabilities (e.g., MLflow with pre-trained models like BGE or DBRX) to generate embeddings for your knowledge base documents.
       • Vector Search: Store these embeddings in Mosaic AI Vector Search indexes. Create Unity Catalog functions that query these vector indexes to retrieve semantically relevant document chunks. Learn how to create and query a vector search index.
     • Code Execution: Create tools that allow the agent to execute Python code in a secure, sandboxed environment (e.g., for complex calculations or data transformations).
     • External API Calls: Wrap external API calls (e.g., to a CRM, payment gateway, or external weather service) within Unity Catalog functions. Leverage Unity Catalog Connections for secure credential management.
   • Document Tools: Ensure your tool definitions include clear descriptions and parameter specifications. This metadata is what the LLM uses to understand when and how to call each tool.

3. Step 3: Authoring the AI Agent

   Databricks offers several ways to author an AI agent:

   • Mosaic AI Agent Framework (Recommended): Databricks provides a native framework for building agents that integrates seamlessly with Unity Catalog, MLflow, and other Databricks services. See the Get started with AI agents tutorial.
     • Define the Agent: Specify the LLM to use (e.g., a served DBRX model or an external API via AI Gateway), the tools it has access to, and its system prompt (persona, instructions).
     • Integrate Tools: Use `UCFunctionToolkit` or similar mechanisms to connect your Unity Catalog functions as tools to the agent.
     • Define Signature: Ensure your agent has a clear MLflow Model Signature for predictable inputs and outputs, which is crucial for deployment and monitoring.
   • LangChain/LlamaIndex Integration: You can also use popular open-source agent frameworks like LangChain or LlamaIndex within Databricks notebooks. Databricks provides specific integrations to use Unity Catalog tools and Databricks-served models within these frameworks.
   • AI Playground (Prototyping): For rapid prototyping and testing, use the AI Playground within Databricks. It offers a low-code UI to select LLMs, add tools, and chat with your agent to test its behavior before moving to code.

4. Step 4: Evaluation and Debugging

   • Iterative Development: Agent development is highly iterative.
   • Logging with MLflow: Use MLflow to log agent runs, including inputs, outputs, tool calls, and LLM prompts/responses. This is essential for debugging and understanding agent behavior. See Track model development using MLflow.
   • Mosaic AI Agent Evaluation:
     • Define Metrics: Use standard metrics (accuracy, relevance) and custom metrics relevant to your use case.
     • LLM Judges: Leverage LLMs to automatically evaluate the quality of agent responses based on predefined criteria.
     • Rule-Based Checks: Implement specific rules to ensure factual accuracy or adherence to safety guidelines.
     • Human Feedback: Set up an Agent Evaluation Review App to collect human feedback from subject matter experts. This feedback can then be used to refine your agent and evaluation datasets.
     • A/B Testing: Evaluate different versions of your agent side-by-side to compare performance.

5. Step 5: Deployment and Monitoring

   • MLflow Model Registry: Register your trained and evaluated agent as a model in the MLflow Model Registry. This provides version control, lineage, and a central hub for managing your agent deployments.
   • Databricks Model Serving (Mosaic AI Model Serving):
     • Deploy as an Endpoint: Deploy your agent from the MLflow Model Registry as a serverless API endpoint using Mosaic AI Model Serving. This provides scalable, low-latency inference. Learn how to deploy and query a custom model.
     • Traffic Splitting: Easily manage traffic between different versions of your agent for A/B testing or gradual rollouts.
     • Built-in Governance: Enforce guardrails, set rate limits, and ensure compliance for your deployed agents.
   • Continuous Monitoring:
     • Payload Logging: Automatically log all inference requests and responses to Delta Lake, enabling auditing and analysis.
     • Performance Monitoring: Track latency, throughput, and error rates of your agent endpoint.
     • Drift Detection: Monitor for data drift or concept drift in the input data or agent’s behavior, which could indicate a need for retraining or refinement.
     • Safety and Content Filters: Implement Mosaic AI Gateway features for content moderation and PII detection to ensure responsible AI usage. See how to configure AI Gateway on model serving endpoints.

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III. Key Databricks Features for Agentic AI

• Unity Catalog: Centralized metadata, governance, and access control for all data (Delta Lake, Vector Search, Functions) and AI assets. Crucial for security and managing agent tools.
• Delta Lake: The open, reliable storage layer for all your structured and unstructured data, providing ACID transactions, schema enforcement, and time travel.
• Mosaic AI Vector Search: A fully managed vector database integrated with Unity Catalog for building powerful RAG applications, ensuring agents have access to relevant enterprise knowledge. See Mosaic AI Vector Search documentation.
• Mosaic AI Gateway: A unified API gateway to manage and govern access to various LLMs (proprietary, open-source, external) used by your agents, with features like rate limiting, logging, and model fallbacks. Read the Mosaic AI Gateway introduction.
• Mosaic AI Agent Framework: Databricks’ opinionated framework for authoring, evaluating, and deploying AI agents, simplifying the development lifecycle.
• MLflow: For comprehensive MLOps: experiment tracking, model registry, and model serving. Essential for managing the entire lifecycle of your AI agents. Get started with MLflow tutorials.
• Databricks Notebooks & Workflows: Collaborative environment for development, orchestration, and scheduling of agent development pipelines.
• Databricks Runtime for Machine Learning: Optimized environments with pre-installed libraries for ML and GenAI development.

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IV. Best Practices for Agentic AI on Databricks

• Start with Clear Use Cases: Define specific problems that an agent can solve with measurable outcomes.
• Iterate and Evaluate: Agentic AI development is iterative. Continuously evaluate performance, collect feedback, and refine your agent.
• Focus on Tooling: The effectiveness of an agent heavily relies on the quality and breadth of its tools. Make sure your tools are well-defined, robust, and secure.
• Data Quality is Paramount: The agent’s performance is only as good as the data it processes and retrieves. Invest in strong data governance and quality practices.
• Security and Governance: Leverage Unity Catalog and Mosaic AI Gateway to ensure data privacy, access control, and compliance throughout the agent’s lifecycle.
• Human-in-the-Loop: While agents aim for autonomy, human oversight and feedback are crucial, especially in early stages and for high-stakes decisions.

By leveraging the comprehensive capabilities of the Databricks Lakehouse Platform, organizations can build, deploy, and manage production-grade Agentic AI systems that drive significant business value.