Dec 14, 2025 · 5 min read
There is a glaring gap between human intelligence and the artificial intelligence we build today. Humans learn continuously. We make mistakes, update our beliefs, acquire new skills, and refine our behavior over time. We get better.
Most AI agents, by contrast, are stuck in a "Groundhog Day" loop. They might have access to incredible world knowledge, but they don't meaningfully improve from their experiences. They are static entities, frozen in time at the moment they were trained.
The traditional answer to this problem has been to focus on the neural network's "weights" the billions of numerical parameters that define the model. Researchers have spent decades trying to figure out how to update these weights on the fly without breaking the model (a problem known as "catastrophic forgetting"). But despite all this effort, modern production LLMs are still deployed with frozen weights.
We are looking for the solution in the wrong place. The breakthrough for creating agents that truly learn doesn't lie in the complex math of weight updates. It lies in a much more accessible and powerful concept: learning in token space.
The old way of thinking assumes that "learning" equals "changing the model." But this approach has proven to be incredibly difficult for practical, deployed agents.
Modern AI agents are not just a model. They are a system composed of the model plus its context. This includes the system prompt (instructions), the tool definitions (capabilities), and the conversation history (memories).
Two identical models given different contexts behave like two completely different people. This realization is the key: instead of trying to change the model (the weights), we should focus on changing the context (the tokens).
This approach has massive advantages:
Right now, most agents use a very primitive form of token learning: they just append every new interaction to the end of the list until they run out of space.
This is not learning; it's logging.
Imagine if your brain worked like this. You wouldn't consolidate memories or learn lessons; you would just have a running transcript of every second of your life until you passed out. This "append-only" approach leads to "context rot," where the agent gets overwhelmed by noise and its reasoning degrades. True learning requires refinement, not just accumulation.
To build agents that actually get smarter, we need to move beyond simple logging and start treating memory management as an active, intelligent process.
Humans consolidate memories while we sleep. We process what happened during the day, discard the noise, and strengthen the important connections. AI agents need a similar process.
We can dedicate "sleep-time compute" to agents background processes that run when the agent isn't talking to a user. During this time, the agent can review its recent experiences, identify contradictions, abstract general patterns from specific events, and rewrite its own memory to be more efficient.
Agents need to be taught to manage their own brains. Right now, models treat their system prompts as static laws. We need to train agents to understand that they can and should edit their own instructions.
If an agent notices that it keeps making the same mistake, it should be able to update its own system prompt to say, "Warning: I tend to fail at X, so be extra careful when doing Y." This makes memory management a first-class tool that the agent can use to improve itself.
The ultimate goal isn't to choose between weights and tokens, but to use them together. Token-space memory provides the fast, flexible, and transparent layer of learning. Over time, stable and universal lessons from this token layer can be "distilled" into the model's weights for efficiency.
This creates a powerful cycle: the agent learns quickly in text, and slowly solidifies that knowledge into its core parameters. But the most valuable asset will always be the learned context the persistent, evolving memory that survives even when the underlying model is replaced.