Will AI Agents Revolutionize How We Query and Use Data?
Snowflake just announced AI Data Agents in Cortex, a new way to automate and streamline data workflows with AI. Instead of just querying data or calling APIs, these agents can dynamically decide which tool to use—whether it's SQL, LLMs, or external APIs—depending on the task at hand.
This feels like a shift from traditional “query-driven” workflows to “goal-driven” automation, where agents choose the best approach rather than relying on static logic.
I’m particularly excited about building agents that select the right tool for the right job—instead of forcing everything into SQL, Python, or one ML model.
What do you think? Will AI-powered agents change how we interact with data? Or will traditional query-based approaches remain dominant?
https://www.snowflake.com/en/blog/ai-data-agents-snowflake-cortex/
7 comments
[ 2.1 ms ] story [ 33.3 ms ] threadThe system uses Claude 3.5 Sonnet running inside Snowflake's secure perimeter for handling complex queries across both structured and unstructured data. This allows maintaining data governance while leveraging LLM capabilities.
Their search stack is using a hybrid approach combining vector and lexical search with semantic reranking. The benchmarks show a 12% improvement over OpenAI embedding models (measured on NDCG@10) using their Arctic-Embed-L-v2.0 model.
For structured data analysis, they're using semantic modeling rather than just pattern matching for Text-2-SQL generation. This helps handle complex multi-table environments and prevents common issues like JOIN hallucinations and double counting.
The quickstart [1] shows how to implement this in practice. Key components:
Agents that can plan multi-step queries Semantic SQL generation for structured data Hybrid search for unstructured content
There's a demo video [2] showing a practical insurance claims scenario that demonstrates:
How it handles complex document collections Real-time semantic understanding of data structures Multi-step query planning and execution Integration between structured DB queries and unstructured document search
Would love to hear from anyone who's worked on similar orchestration challenges across structured and unstructured data securely, and gain meaningful insights.What challenges have you encountered with mixing structured and unstructured data processing?
[1] https://quickstarts.snowflake.com/guide/getting_started_with... [2] https://www.youtube.com/watch?v=SDqyB-yWaAE
How well does this work if we give it 10+ search services, SQL data models, python tools, etc. any benchmarks or first-hand experience?
This really comes down to your tool design. It's more about crafting really high quality descriptions and examples versus worrying about any kind of prompting directly to the agent about usage. Make the tool highly self evident and the agent will naturaly use it.