Medium LLM
8/10 signal
I Took the “Build Data Agents on Snowflake” Course.
agenticevaltool-usemulti-agent
What happened
The author reviews DeepLearning.AI's 'Build Data Agents on Snowflake' course. While praising the evaluation framework (a hierarchical LangGraph setup: planner -> executor -> specialized tools), they criticize Snowflake's built-in AI agent, which failed during the course's own demos. The author advocates for a decoupled architecture where a frontier coding agent (like Claude Code) handles reasoning and connects to Snowflake purely as an auditable data warehouse.
Why it matters
It exposes the limitations of database-native AI agents and advocates for a robust, decoupled architecture using frontier models and open orchestration.
The take
This is a fantastic, highly practical reality check. It exposes the fragility of vendor-locked 'AI features' and validates a critical architectural pattern: decouple your reasoning engine (using frontier models) from your data layer. The recommendation to steal the course's LangGraph evaluation framework while ignoring the proprietary Snowflake AI tooling is highly actionable advice.
Do this
Keep your data agents decoupled: use frontier models (like Claude) for planning and reasoning, and treat your database strictly as a tool. Check out the DeepLearning.AI course specifically to adopt its LangGraph evaluation framework.
Don't read this site daily. Get it in your inbox.
The daily brief and Sunday deep dive — distilled, scored, and opinionated. For builders only.