Why an AI Keeps Rechecking the Door Lock

Anchor: Door-Lock Rechecking — across independent runs, an LLM agent spontaneously re-submitted the same critical artifact, without being told to.

In a multi-agent system, we observed a behavior nobody explicitly taught:

After completing a critical artifact, the agent called the submission tool again and re-submitted the same file. The content didn’t change. The hash was identical. Nothing in the prompt asked for a second submission.

After you leave home, have you ever stopped, turned around, and rechecked the door lock?

This isn’t about memory.

54 Multi-Agent Runs Under One Fixed Protocol: Behavioral Fingerprints and a Coordination Blind Spot

Engineering note: this post is based on limited samples and is focused on detectable failure modes, not benchmark rankings.

Different LLMs don’t just perform differently. Under the same multi-agent protocol constraints, they collaborate differently.

We ran 54 multi-agent sessions across 4 provider configurations under identical protocol constraints. The behavioral fingerprints were reproducible and revealed that protocol compliance (emitting the right signals in the right order) is a major differentiator in agentic settings.