Your AI Agents Need an Accountability Layer

You shipped a multi-agent system. Agents route tasks, process data, produce outputs. It works. Stakeholders are happy. Then someone from compliance shows up. "Which agent made this decision? What data did it have access to? Can you prove nothing was modified after the fact?" You check your logs. They're there. But they're mutable. Any process with write access could have altered them. You have observation, not evidence. That distinction is about to matter more than most teams realize. The Accountability Gap Most agent systems have extensive logging. Print statements, structured JSON, maybe a centralized log aggregator. That covers observability. Accountability is a different question. Can you prove what happened? Can you demonstrate that the record is complete, unaltered, and attributable to a specific actor? Traditional logging fails this test for three reasons: Mutability. Logs can be edited, truncated, or deleted. If an agent (or a compromised process) modifies a log entry, there's