u/noasync

How do you give an AI agent a memory that is both durable and governed?

We just published a guide to building stateful agent memory on Snowflake using Cortex features and relational primitives to model a knowledge graph. This provides agents with durable, trust-aware recall without adding a dedicated graph database.  

The end-to-end stack:

• Pipeline: Streams + Tasks + AI_EXTRACT. It’s declarative and runs under the same Snowflake Horizon primitives as the rest of our warehouse.
• Memory: Instead of a specialized graph database, we used Relational Tables + Vector columns. Traversal is handled by Recursive CTEs.
• Discovery: Cortex Search provides hybrid retrieval (vector + keyword) with RRF (Reciprocal Rank Fusion).
• Orchestration: We’ve replaced custom orchestration logic with Cortex Agents used as declarative tools.

The result: agent recall is durable and, more importantly, auditable.

https://www.capitalone.com/software/blog/scaling-agent-context-snowflake-knowledge-graphs/?utm_campaign=scaling_context_ns&utm_source=reddit&utm_medium=social-organic

Most AI agents are tested on toy data (clean, verified datasets). Here is what happened when Cortex Code was hit with 55.8 billion rows:

• The Win: It understands the Snowflake "secret menu" (Bloom filters, pruning).
• The Surprise: It built a multi-channel dbt project without being told the connections.
• The Difference: General LLMs know SQL syntax. CoCo knows the Snowflake platform.

If you’re just using AI for syntax, you’re missing the point. The value is in the native platform intelligence.

Read our full review here:
https://www.capitalone.com/software/blog/snowflake-cortex-code-cli/?utm_campaign=coco_ns&utm_source=reddit&utm_medium=social-organic