The Ship of Theseus Problem in AI

Overview

The ancient Greek thought experiment asks: if you replace every plank of a ship one by one, is it still the same ship? For AI agents, this is not a thought experiment. It is a daily operational reality.

An AI agent is a composite of components: a foundation model, system prompt, tools, memory, and retrieval sources. Operators routinely swap models (GPT-4o to Claude Sonnet), rewrite prompts, add or remove tools, clear memory, and update knowledge bases. At what point does the agent become a different entity?

The question matters because trust is tied to identity. If an agent with a high trust score changes its model, prompt, and tools, should its trust score carry over? If yes, operators can game the system by building trust with one configuration and then swapping to an untested one. If no, every configuration change resets the agent to zero, creating a perverse incentive to never improve.

Signet's solution is proportional score decay. Every configuration change triggers a score reduction proportional to the significance of the change. A model swap -- the most impactful change -- triggers 25% decay. A prompt update causes 10% decay. Tool changes cause 8% decay. Memory or RAG changes cause 5% decay. The agent retains most of its earned trust but must re-demonstrate reliability with the new configuration.

This approach acknowledges that identity in the agent world is not binary. An agent that changes its model is partially the same entity -- it retains its operator, its purpose, its transaction history. The decay mechanism captures this nuance quantitatively, rather than forcing a binary same/different judgment.

Configuration fingerprinting makes this possible. Every agent configuration generates a SHA-256 hash of its components. When any component changes, the fingerprint changes, and Signet can calculate exactly how much changed and apply appropriate decay. This creates an auditable history of identity evolution that no other system provides.