AI Agents Learn and Adapt in Real-time During Operation

Current AI agents typically operate with pre-trained models where the core weights are frozen upon deployment. Any subsequent intelligence or adaptation is built around these static models through external mechanisms like prompts, tools, retrieval systems, and memory. This architecture works well for discrete interactions, such as chat applications, where each query has a clear beginning and end. A new paradigm, exemplified by systems like AdaJEPA, proposes a shift where the agent's internal world model is updated within the control loop itself. Instead of relying solely on external scaffolding, the agent takes an action, observes the actual outcome, and then subtly adjusts its latent model to better reflect reality before making its next decision. This continuous, real-time learning eliminates the need for full retraining runs, larger context windows, or complex memory tricks, allowing the model to become incrementally more accurate with each interaction.