Where do Agentic AI agents store their learning?

Agentic AI agents store their learning in several ways, primarily depending on the architecture of the system and the type of learning involved. Here’s an overview of how and where these agents typically store their learned information: 

  1. Model Parameters: The core of an AI agent's learning is stored in the parameters (or weights) of its model. These parameters are adjusted during the training process to minimise errors or maximise rewards, depending on the learning algorithm used (such as supervised learning or reinforcement learning). For neural networks, these parameters are the weights of the connections between neurons. 
  2. External Databases: For tasks that require the AI to reference or update large sets of data, or to retain information over long periods, learning and operational data can be stored in external databases. This allows the AI to access and modify information based on new inputs or decisions made during operation. 
  3. On-Device Storage: In scenarios where AI agents operate on specific hardware or local systems (like robots, smartphones, or embedded systems), the learning might be stored locally to facilitate quick access and real-time decision-making. This is crucial for applications requiring operational independence or those with strict data privacy concerns. 
  4. Cloud Storage: Often, especially for models deployed in scalable environments, the learned parameters and additional necessary information (like training data or model metadata) are stored in the cloud. Cloud storage offers flexibility, scalability, and the ability to update and synchronise learning across multiple instances of an agent. 
  5. State Representation: For agents employing reinforcement learning, the knowledge is often encoded in a state-value table or a policy model that maps given states of the environment to actions that the agent should take. These representations can be stored similarly to model parameters but are specifically tuned to capture the dynamics of the environment in which the agent operates. 
  6. Knowledge Graphs and Ontologies: In more complex systems, particularly those that need to understand and manipulate large amounts of structured knowledge, learning may be stored in the form of knowledge graphs or ontologies. These structures allow the AI to reason about the relationships between different pieces of information and make informed decisions based on logical deductions. 
  7. Hybrid Storage Solutions: Some advanced agentic AI systems use a combination of these storage methods to optimise both performance and scalability. For example, real-time processing might occur with on-device storage, while long-term learning and large-scale data analysis might utilise cloud-based systems.