Abstract
A potential issue in artificial intelligence (AI) agent development is cross-context regression, where a modification to an agent's core rulebook, or system prompt, may cause unintended failures in previously functional areas. A system for historical regression testing can address this by processing proposed rulebook changes before deployment. The system can maintain a datastore of time capsules, which are records that may contain historical user prompts, pointers to specific codebase states, and tests that indicated successful outcomes. When a rulebook is modified, a state reconstruction engine can use these capsules to dynamically rebuild past environments in isolated sandboxes, such as containers or virtual machines. The AI agent can then attempt the original task with the new rules. This automated framework can help mitigate regressions by verifying if updates degrade the AI's demonstrated ability to solve past problems, potentially promoting a more stable evolution of its capabilities.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Muthukumar, Vishak, "Historical Regression Testing of AI Agents Using Dynamic State Reconstruction", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9778