Agentic AI in Action — Part — 22 — Memory in Agentic AI on Snowflake

2026-06-17 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

The article explores how integrating memory transforms stateless AI models into adaptive "agentic AI" systems, specifically demonstrating this within Snowflake's AI Data Cloud. It highlights that traditional AI models are stateless, reacting only to immediate context, whereas agentic AI observes, reasons, acts, and remembers, building continuity and refining behavior. The proposed architecture leverages Snowflake Cortex capabilities to store memory as both structured records (timestamps, actions) and semantic embeddings, enabling retrieval of contextually relevant past experiences via cosine similarity. This allows agents to generate informed responses and consolidate individual events into higher-level understanding. The implementation details setting up a memory table, storing interactions with embeddings, and retrieving them to inform LLM responses, emphasizing a self-reinforcing cycle of continuous improvement.

Key takeaway

For AI Architects designing adaptive enterprise AI systems, integrating robust memory architectures is critical to move beyond reactive tools. You should implement both structured and semantic memory within platforms like Snowflake's AI Data Cloud, leveraging capabilities like Cortex for embedding generation and semantic retrieval. This approach ensures your agents accumulate experience, recognize patterns, and continuously refine their behavior, transforming them into truly intelligent, context-aware teammates rather than isolated responders.

Key insights

Memory transforms AI from a reactive tool to an adaptive, continuously learning participant by enabling experience accumulation.

Principles

• Intelligence is defined by the ability to accumulate experience.
• Agentic systems operate in a continuous observe-reason-act-evaluate-update loop.
• Semantic memory enables retrieval based on meaning, not keywords.

Method

Implement agent memory by storing interactions as structured data and semantic embeddings in a memory table, then retrieve relevant past experiences using cosine similarity to inform LLM reasoning.

In practice

• Store customer interactions with embeddings for context.
• Use cosine similarity to retrieve relevant past events.
• Consolidate memory events into higher-level summaries.

Topics

• Agentic AI
• Snowflake AI Data Cloud
• Semantic Memory
• Cortex Functions
• LLM Context
• Vector Embeddings

Code references

• Krishsriniv/Snowflake-Agentic-Memory

Best for: AI Engineer, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.