Researchers have introduced MemoHarness, a framework for agent harnesses that learn from experience. The paper focuses on how agents can improve across repeated interactions rather than treating every task as isolated.
That matters because many agent failures come from forgetting prior outcomes, repeating mistakes, or lacking durable task context. A harness that captures experience could make agent workflows more reliable over time.
The work reflects a broader shift from single-prompt agents toward systems with memory, evaluation, and feedback loops around the model.