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Exploring continual learning in AI reveals a layered approach beyond just tweaking model weights, involving the harness and context to create more adaptable and intelligent agents over time.
When we talk about continual learning in AI, most discussions focus on updating model weights. However, for AI agents, the learning process can happen at three distinct layers: the model itself, the harness that powers it, and the context that configures it. Understanding these layers changes how you approach building systems that improve over time.
When people discuss continual learning, they often mean updating model weights. Techniques for this include Supervised Fine-Tuning (SFT), Reinforcement Learning (RL) methods like Guided Reinforcement Policy Optimization (GRPO), and others.
A significant challenge here is catastrophic forgetting-when a model trained on new data or tasks degrades in performance on previously learned tasks. This remains an open research problem.
For specific agentic systems, such as OpenAI's Codex models, training is typically done for the entire system. In theory, you could fine-tune models at a more granular level (e.g., using Low-Rank Adaptation (LORA) per user), but in practice, this is usually done at the agent level.
The harness includes the code that drives the agent and any instructions or tools that are always part of it. As harnesses have gained popularity, several papers have explored how to optimize them.
One notable example is Meta-Harness: End-to-End Optimization of Model Harnesses. The core idea is that the agent runs in a loop. You first run it over multiple tasks and then evaluate its performance. This process helps identify areas for improvement in the harness, such as optimizing code efficiency or enhancing tool integration.
The context layer involves additional instructions or skills that can be used to configure the agent. These can be updated independently of the model and harness, allowing for more flexible and dynamic learning.
For example, a user might add new skills to an AI coding assistant by updating a configuration file (e.g., CLAUDE.md). This approach allows the agent to adapt to new tasks or environments without requiring changes to the underlying model or harness.
Understanding these three layers of continual learning is crucial for building more adaptable and robust AI agents. By focusing on all three layers, you can create systems that not only improve over time but also remain effective across a wide range of tasks and environments.
By leveraging continual learning at all three layers, you can build AI agents that are more resilient, flexible, and capable of handling a diverse set of challenges.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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6 April 2026
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