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Hey community! Imagine having a smart assistant at work that doesn't just answer simple questions. It can think step by step, decide what to do next, use different tools, and even go back if something goes wrong. That's what AI agents do. They are like helpful robots that handle tasks on their own, making decisions along the way.

LangGraph is a tool created by the same team behind LangChain. It helps you build these smart agents easily. Unlike basic chains that go in a straight line, LangGraph lets you create workflows with loops, branches, and memory. The AI can remember past steps, make choices, and keep trying until it gets the right answer. This makes agents more reliable for real-world jobs, like analyzing data or answering complex questions.

Both Snowflake and Databricks are great platforms for big data, and they pair really well with LangGraph. You can build powerful agents right inside these systems, keeping everything secure and fast.

Why LangGraph is Perfect for AI Agents

Simple AI apps might just take a question and give an answer. But agents need more. They have to reason, call tools (like running a database query), check results, and sometimes loop back. LangGraph handles this with a graph structure – think of it as a map with nodes for actions and edges for decisions.

For example, an agent might:

• Read a user's question.
• Decide if it needs to search documents or run a calculation.
• Use a tool to get info.
• Think about the result.
• Ask for more if needed.

LangGraph makes this control easy. It also adds state, so the agent remembers the conversation. Plus, it's open-source and works smoothly with LangChain tools.

Using LangGraph on Snowflake with Cortex AI

Snowflake is a cloud data platform that's fast and easy to scale. Its Cortex AI brings powerful language models right into your data warehouse. You don't have to move data out – everything stays safe inside Snowflake.

With the langchain-snowflake package, connecting LangGraph to Cortex is straightforward. Cortex has functions for completing text, searching, and even analyzing structured data.

Here's how it works:

• Use Cortex LLM functions (like COMPLETE or AI_COMPLETE) as your model's brain.
• Add tools for Cortex Search (great for finding info in documents) or Cortex Analyst (turns natural questions into SQL queries).
• Build your graph: One node for the agent to think, another for tools, and edges to route based on decisions.

A real example is a customer support agent. A user asks about an order. The agent decides: Is this about structured data (like order status in tables)? Use Cortex Analyst to query. Or unstructured (like emails)? Use Cortex Search.

People have built multi-agent setups where one agent routes to others – all powered by models like Mistral or Meta inside Cortex. No data leaves Snowflake, and it's serverless, so you only pay for what you use.

If you're on Snowflake, start with their quickstarts. Install langchain-snowflake, set up a chat model with Cortex, define tools, and graph it out. It's simple code that runs right in Snowflake notebooks or apps.

Using LangGraph on Databricks

Databricks is built for data engineering and machine learning. It has strong tools like Unity Catalog for managing data and functions securely.

LangGraph fits perfectly here. Databricks has direct support through packages like databricks-langchain and Unity Catalog integrations.

Key features:

• Turn Unity Catalog functions (SQL or Python) into tools for your agent automatically.
• Use hosted models like Llama or Mixtral via Databricks endpoints.
• Track everything with MLflow – it logs traces of your agent's steps, so you can debug loops or see where it went wrong.
• Mosaic AI Agent Framework works with LangGraph without rewriting code.

For instance, build a data analyst agent. Ask: "What's our sales trend last quarter?" The agent uses Unity Catalog tools to run queries safely, pulls from vector search for docs, and explains results.

Databricks even has examples in their docs and notebooks. Install the packages, enable MLflow tracing (just one line: mlflow.langchain.autolog()), define your graph, and run. Tracing shows each node, tool call, and LLM response – super helpful for fixing issues.

Companies like 7-Eleven use this setup for creative assistants, with multiple agents collaborating via LangGraph.

A Simple Example: Data Analyst Agent

Picture this common setup on either platform:

1. User asks in plain English: "Show me top customers and why sales dropped."
2. Agent thinks: Need structured data query + maybe document search for reasons.
3. Calls tool: Runs SQL via Cortex Analyst (Snowflake) or Unity Catalog (Databricks).
4. Gets results, summarizes, checks if more info needed.
5. Loops if unclear, or answers with charts/explanations.

LangGraph manages the back-and-forth. On Databricks, MLflow traces it. On Snowflake, Cortex handles security.

Benefits of These Setups

• Secure: Data never leaves the platform. Governance from Unity Catalog or Snowflake roles applies.
• Reliable: Graphs ensure the agent follows rules every time, reducing wrong answers.
• Easy to Improve: Tracing and checkpoints let you replay runs and fix problems.
• Scalable: Serverless on both – handle many users without worry.
• Cost-Effective: Pay per use, no extra servers.

Getting Started Tips

If you're new:

• Pick one platform you're already using.
• Install LangGraph and the integration package.
• Follow official docs or quickstarts – they have ready notebooks.
• Start small: One tool, like SQL query.
• Add complexity: Loops, multiple tools, memory.

In 2025, AI agents are changing how we work with data. LangGraph on Snowflake or Databricks makes it accessible – no need for expert AI teams. You get smart, secure helpers that boost productivity.

Ready to try? Pick a simple task in your data, build a basic graph, and watch it come alive. The results will surprise you! Thanks for reading! More awesome blogs are on the way with SightSpeak AI, so stay tuned for what’s next!