# Agentic AI in Action — Part — 22 — Memory in Agentic AI on Snowflake > Source: > Published: 2026-06-17 04:12:58+00:00 Most AI models today are inherently stateless. While systems can introduce memory through external storage and retrieval, the model itself does not persist experience across interactions. Every interaction depends on what is explicitly provided in the moment, rather than what the system has accumulated over time. Context can be passed. Experience cannot. This distinction is subtle, but important. A stateless system can respond intelligently within a moment, but it does not naturally improve from one interaction to the next. It does not remember recurring issues. It does not recognize patterns unless they are explicitly retrieved and supplied again. Without persistent memory, intelligence exists only within the boundary of a single request. Agentic AI changes this model. An agent does not simply respond. It observes, reasons, acts, and critically, remembers. Memory allows the system to carry forward experience, build continuity, and refine its behavior over time. Each interaction becomes part of a growing internal context that influences future decisions. This is what transforms an AI system from a reactive tool into an adaptive participant. When a customer support agent remembers prior complaints, resolution patterns, and customer preferences, its responses evolve from generic to precise. When a data engineering agent remembers schema changes, pipeline failures, and data anomalies, it prevents recurring issues instead of repeatedly reacting to them. When an enterprise AI system remembers organizational context, it stops behaving like an external assistant and starts behaving like an internal teammate. Memory transforms AI from reactive to adaptive. The reason is simple. Intelligence is not defined by reasoning alone. It is defined by the ability to accumulate experience. Agentic systems operate in a continuous loop. They observe inputs, reason about them, take actions, evaluate outcomes, and update their internal state. Memory is the persistence layer of this loop. It carries forward the system’s accumulated understanding, ensuring that every future decision benefits from past experience. Without memory, the loop collapses into repetition. With memory, the loop becomes evolution. This memory exists in multiple forms. Some memory is short-term, preserving context within a session. Some memory is long-term, persisting across sessions and workflows. Some memory is structured, such as events, decisions, and system states. Some memory is semantic, capturing meaning through embeddings that allow agents to retrieve relevant past knowledge even when the wording differs. Together, these layers create continuity. Modern data platforms make this architecture practical. Snowflake’s AI Data Cloud provides a unified foundation where agent memory, reasoning, and execution coexist securely and scalably. Memory can be stored as structured records, enriched with semantic embeddings, and retrieved dynamically using Cortex functions. This allows agents to access relevant historical knowledge in real time, without introducing external complexity or fragmentation. The architecture becomes both intelligent and governed within the AI data cloud. The lifecycle begins when the agent observes an interaction. This interaction is converted into both structured and semantic memory. **Structured memory** captures explicit attributes such as timestamps, users, actions, and outcomes. **Semantic memory** captures meaning using embeddings, enabling contextual retrieval later. When the agent encounters a new situation, it queries its memory store to retrieve the most relevant prior experiences. These retrieved memories inform reasoning, enabling more accurate decisions. After the action is completed, the outcome itself becomes new memory, continuously improving the agent. This creates a self-reinforcing cycle. The implementation of this architecture can be achieved entirely within Snowflake using Cortex capabilities. In this implementation, the agent combines structured and semantic long-term memory, allowing it not only to store experiences, but to retrieve and apply them intelligently when needed. We begin by establishing the environment where agent memory will live. This creates a governed, centralized memory layer inside Snowflake. Memory itself consists of two components. The event itself, and the semantic representation of that event. The event captures what happened. The embedding captures what it means. We now create the memory table. This table becomes the persistent memory of the agent. Every time the agent observes an interaction, it converts that interaction into semantic memory and stores it. This insert statement stores a customer interaction in the agent’s memory table and generates a semantic embedding of the interaction using an embedding model. The embedding converts the text into a numerical vector that captures its meaning, allowing the agent to later retrieve this memory based on contextual similarity rather than exact wording. At this moment, the agent has learned something. The interaction is no longer transient. It has become persistent experience. As more interactions occur, the memory grows. Let us insert another row. The power of memory emerges during retrieval. When a new query arrives, the agent searches its memory for relevant prior experiences using semantic similarity. This query converts the new customer issue into an embedding and compares it with stored memory embeddings using cosine similarity to find the most contextually relevant past events. It allows the agent to retrieve prior experiences that are semantically similar, enabling more informed and context-aware responses. This allows the agent to retrieve experiences based on meaning, not keywords. Even if the wording differs, the context remains accessible. This retrieved memory now becomes input to reasoning. Now, the query below retrieves the agent’s memory events and provides them as context to the Cortex LLM (claude-3–5-sonnet) enabling it to generate a response that is informed by prior interactions rather than responding in isolation. In effect, it allows the agent to use its stored memory to produce a more context-aware and intelligent response. This response is fundamentally different from a stateless answer. It incorporates accumulated experience. It reflects continuity. The agent is no longer reacting in isolation. It is responding with context. It allows the agent to convert multiple past experiences into a higher-level understanding, helping it recognize patterns instead of treating each event independently. *(Note: If the model claude-sonnet-4–5, is not supported in your region, you may get an error during the execution. To resolve this, please enable the CROSS_REGION_INFERENCE parameter.Setting the parameter to **ANY_REGION provides access to the full set of supported models and Cortex products, with the highest available capacity. Snowflake routes requests to the optimal region automatically. Refer **here** for details on the same.* *ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = ‘ANY_REGION’;)* Moving ahead, the query below combines all support-related memory events into a single text using LISTAGG, and then uses the Cortex SUMMARIZE function to generate a concise summary of those interactions. Over time, memory itself evolves. Individual events can be summarized into higher-level understanding. Cortex enables this consolidation. This allows the agent to move beyond remembering individual events and begin understanding patterns. In a production setup, this architecture expands beyond a single memory table into a fully managed memory lifecycle integrated with governance, performance optimization, and operational reliability. Memory is typically separated into short-term operational memory and long-term semantic memory, with automated pipelines that continuously ingest interactions from applications, support systems, or data platforms. Retrieval is optimized using vector indexes and relevance filtering to ensure low-latency access even at scale. Memory is periodically summarized, consolidated, and pruned to prevent uncontrolled growth and preserve only high-value knowledge. Guardrails and access controls ensure that sensitive memory is only accessible to authorized agents, maintaining enterprise security and compliance. Observability layers track what memory was retrieved, how it influenced decisions, and whether outcomes improved, enabling continuous evaluation and refinement. Over time, this transforms the agent from a reactive responder into a continuously learning system that becomes more accurate, efficient, and context-aware as it operates. Memory transforms the nature of the system itself. Without memory, the agent behaves like a calculator, producing outputs without awareness of history. With memory, the agent behaves like a participant, continuously refining its understanding of its environment. This is the true shift introduced by Agentic AI. While this demonstrates how easily you can implement this loop using the Snowflake AI Data Cloud, the fundamental concept of architectural memory is entirely generic and can be applied to any data ecosystem. The future of enterprise AI will not be defined by models alone. Models provide reasoning, but memory provides continuity. Models generate responses, but memory enables learning. Models create intelligence in isolation, but memory creates intelligence over time. Agentic systems are not defined by their ability to respond. They are defined by their ability to remember. And through memory, to improve. The code associated with this blog can be accessed [here.](https://github.com/Krishsriniv/Snowflake-Agentic-Memory) [Agentic AI in Action — Part — 22 — Memory in Agentic AI on Snowflake](https://pub.towardsai.net/memory-in-agentic-ai-on-snowflake-how-memory-transforms-ai-from-tool-to-teammate-b6518a7d926a) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.