Static Agents Are Already Legacy Code

This article discusses the limitations of static AI agents and the importance of building systems that can continuously learn and adapt to changing environments and user needs.

Details

The article argues that the traditional approach of training a model, evaluating it on benchmarks, and deploying it behind an API fails for agents operating in open-ended environments. ALTK-Evolve, a recent IBM Research project, formalizes the concept of agents that can learn and update their behavior at runtime based on task outcomes, user feedback, and other signals. This is a fundamentally different architecture where learning is a first-class runtime operation, unlike retrieval-augmented generation bolted onto a static model. The key insight is that useful adaptation can be achieved without gradient updates, by using structured feedback to update policy representations, select better tool combinations, and refine planning strategies. However, implementing on-the-job learning requires robust data infrastructure to capture, filter, and incorporate new experiences without creating feedback loops or privacy issues. Adaptive agents also introduce new challenges like catastrophic forgetting and the need to weigh user feedback, which require careful system design and architectural considerations. The article emphasizes that building adaptive agents requires thinking less about 'model capabilities' and more about 'system dynamics', as the focus shifts from shipping a model to shipping a learning loop with safety constraints.