Where Accountability Breaks When Agents Multiply

Short summary

When multiple AI agents collaborate, accountability breaks down because each agent's logs are self-reported and can't serve as neutral evidence. Internal records, no matter how detailed, suffer from a position problem similar to a company keeping its own books without external audit. The solution is to fix decision boundaries outside the agent before execution, creating an external declaration of who decided what and when.

• •Multi-agent accountability fails because self-kept logs can't serve as neutral evidence
• •Internal records have a position problem analogous to unaudited financial books
• •Solution: fix decision boundaries externally before agent execution, not after

Generated with AI, which can make mistakes.