Mastering Agent Memory Management
Exploring strategies how to use memory for your autonomous agent.
Introduction
When building Autonomous Agent solutions that provide fast access to your solution managing memory is critical.
Why?
Before we start. I will be introducing some tools for memory management in one of my future code-clinics. So, to stay up-to-date you need to hit that subscribe button. Secondly, memory in the context of autonomous agents does not refer to the hardware Random Access Memory (RAM) nor any caching technologies like Redis or Memcached.
Memories
What I refer to by the term “memory” is the autonomous agent’s capacity to store and retain information, experiences, and acquired knowledge for making informed decisions and actions.
Autonomous cars and industrial robots need to be able to make a decision fast in a setting that is likely to be new.
In a real-world setting, you don’t have 10 seconds to decide if you want to run over Grandma or not.
Please don’t drive over any Grandma. Source: Author - Stable Diffusion as you can see on the right eye.
If we understand memory in the context of autonomous agents as their ability to retain and retrieve information from past experiences, then we can infer that memory enables our agents to learn, adapt, and make informed decisions by incorporating insights from their previous interactions with the environment they operate in.
Why?
With machine learning, autonomous agents gain capabilities like object recognition, scene detection, translation, question/answering, transcription, or semantic search.
But with memory, they can semantically contextualize Search, Anomaly Detection, and Classification.
Search (where results are ranked by relevance)
Clustering (grouping by similarity — a flock of seagulls)
Recommendations (where items or actions are recommended)
Anomaly detection (where outliers with little relatedness are identified)
Diversity measurement (where similarity distributions are analyzed)
Classification (classify by their most similar label)
All of these activities are highly data-intensive operations that might include, sensory data (trajectory data), learned scene patterns, task outcomes, and contextual understanding.
The problem is that traditional methods of memory storage and retrieval in SQL and NoSQL databases are often inefficient when working with these complex and high-volume data structures.
But why is that?