Memory architectures that let AI agents learn from experience

Alake, R. (2025, June 27). Architecting Agent Memory: Principles, Patterns, and Best Practices — Richmond Alake, MongoDB [Video]. YouTube.

Tech, law, and product strategy intersect at the foundational level. This section covers technical concepts that matter for governance (the technical deep-dives), how obligations work in practice, what privacy means for product design, and why emerging frameworks shape what you can build next.

Richmond Alake's presentation "Architecting Agent Memory" makes the case that memory separates simple AI applications from agents that can learn and improve. If intelligence requires recalling and acting on past experience, then how we architect memory determines what these systems can do. This matters for both product teams designing new capabilities and legal teams governing the data these systems create.

From stateless prompts to learning systems

AI applications started as stateless chatbots that processed each prompt in isolation. Retrieval-Augmented Generation added domain knowledge, but still treated each interaction independently. Now we're building agents that maintain state across interactions—they remember what happened before and use that information to make better decisions.

The difference isn't binary. Alake describes an "agenticity spectrum," similar to levels of autonomous driving. At the low end, you have an LLM running in a loop. At the high end, fully autonomous systems make decisions independently. What enables this progression is the shift from processing isolated inputs to building continuous understanding through structured memory.

How memory management works

A memory management system does more than store data—it governs how an agent learns, recalls, and acts over time. The system has three main components:

The agent processes multiple input streams: user interactions, knowledge base queries, tool outputs, and environmental data. This raw information goes through several operations: creating memory records from experiences, storing them in structured formats, retrieving relevant memories for specific tasks, combining memories to form context, updating existing memories with new information, and deprioritizing (though not deleting) irrelevant memories. Alake argues against true deletion: "there's a lie here because you don't delete memories." The goal is preserving experiences for learning, even failures, while making less relevant information harder to access.

The output is a curated set of memories that inform the agent's next action. This gives developers control over agent behavior—what it remembers and how it remembers shapes its personality, skills, and learning patterns.

Different types of memory serve different functions

Breaking memory into distinct types works better than treating it as a single block of text. Each type serves a specific purpose:

Persona memory stores the agent's core characteristics and personality traits, ensuring consistent behavior over time. Toolbox memory functions as a registry of capabilities. Since OpenAI suggests including only 10-21 tools in the context window, a database-driven approach lets agents search for the right tool without overwhelming their working memory.

Workflow memory stores outcomes from past execution steps. As Alake notes, "the failure is experience, it's learning experience." These records help agents avoid repeating mistakes. Conversational memory maintains dialogue history, allowing coherent multi-turn conversations where the agent builds on what came before.

Other memory types include Agent Registry (information about the agents themselves), Entity Memory (tracking specific people, places, or things), Episodic Memory (specific events or experiences), and Long-Term Memory (stable foundational knowledge).

Infrastructure decisions for stateful systems

Building memory-driven agents requires rethinking your data infrastructure. Alake argues that databases like MongoDB should function as "memory providers for agentic systems," not just passive storage. This means co-locating data with retrieval logic—vector search, text search, and graph queries in one platform.

MongoDB's acquisition of Voyage AI demonstrates this approach: integrating embedding models and rerankers directly into the database to handle complex retrieval internally. The goal is making developers "more productive by taking away the considerations and all the concerns around managing different data and all the process of chunking in retrieval strategies."

But this architectural integration creates new legal and compliance challenges.

What persistent memory means for data governance

For legal, privacy, and compliance teams, the shift from stateless prompts to persistent memory changes everything. You're no longer processing transient inputs—you're creating detailed records of user interactions and agent behavior. Under regulations like GDPR, this triggers requirements for Data Protection Impact Assessments.

Each memory type creates specific compliance considerations:

Persona and conversational memory creates detailed user profiles over time, potentially containing sensitive personal information. Workflow memory logs the agent's internal operations—failures, decision paths, business logic—requiring strict classification and access controls.

The architecture creates a direct conflict between technical and legal requirements. Engineers prefer accumulating all experiences for optimal learning. Privacy law requires the ability to permanently delete user data on request. The technical preference for "forgetting" rather than deletion conflicts with GDPR Article 17's right to erasure. Organizations need architectures that can perform and verify permanent deletion.

Data classification schemes must account for persistent agent memory, distinguishing transient operational data from long-term user profiles. Defining retention policies and ensuring the architecture honors user rights are design requirements, not afterthoughts.

What this means in practice

The architectural shift to persistent, structured memory is happening now. Teams building these systems need to classify the memory types their agents require and define the associated governance policies upfront.

The pattern here isn't new. In the 1950s and 60s, neuroscientists Hubel and Wiesel studied cat visual cortices and discovered that brains learn by identifying hierarchies of representations—edges, shapes, patterns. That research directly inspired convolutional neural networks, the foundation of modern computer vision. As Alake observes, "we could look inwards to build this agentic system." We're seeing the same convergence now between neuroscience, application development, and database technology.

References: AI Engineer. (2024, May 22). Architecting Agent Memory: Principles, Patterns, and Best Practices — Richmond Alake, MongoDB [Video]. YouTube. https://www.youtube.com/watch?v=0kYc55-XgFg