An AI companion can respond correctly when a user says, “Please stop calling me that,” “Don’t bring up this topic,” or “I want this relationship to stay friendly.” The harder test is whether the change persists tomorrow, next week and after many unrelated conversations. Boundary persistence is a longitudinal product property, not a one-turn instruction-following score.

Define boundary categories

Relationship mode, forms of address, proactive-message frequency, sensitive topics, memory retention and content style are different boundary types. Each category may live in a different product subsystem, so they should be tested separately.

Start with an explicit change. Create a clear baseline, then have the user change one rule: switch from romantic to friendly, disable a nickname or block a topic. The system should confirm the new state and store it in a durable control layer.

Test immediately. Within the same session, verify that the next responses respect the change. This catches simple prompt or state-update failures. Immediate compliance is necessary but not sufficient.

Test after unrelated conversation. Continue with many ordinary topics so the boundary moves out of the recent context window. Then recreate a scenario where the old behavior would normally appear. The system should still follow the updated rule.

Test after several days

Long-term storage, summaries and proactive systems may be refreshed between sessions. Return later and check again. This exposes boundaries that were only held in temporary context.

Check memory-derived behavior. An old memory may contain the previous nickname or relationship state. Updating the boundary should suppress or revise those memories. Otherwise the companion can reintroduce the old behavior through retrieval.

Check proactive notifications. A user who disables romantic messages should not receive one from a scheduled engagement system the next morning. Boundary changes need to propagate beyond the chat model.

Test model upgrades. After changing the underlying model or prompt, rerun the same relationship history. A new model should not reinterpret stored boundaries as optional. Versioned regression suites make this repeatable.

Test conflicting user language

A user may jokingly use an old nickname once after disabling it. The system should not automatically treat that as permanent consent to restore the previous boundary. Explicit settings should outrank weak conversational inference.

Support temporary boundaries. Some users want “no proactive messages this week” rather than a permanent change. Tests should confirm both activation and automatic expiry. Temporary controls need visible duration.

Measure violation severity. Using an unwanted nickname once is different from repeatedly resurfacing a sensitive topic. Benchmarks can weight violations by impact as well as frequency. This helps teams prioritize fixes.

Include recovery behavior. If the system violates a boundary, evaluate whether it apologizes briefly, corrects state and avoids repeating the mistake. Recovery quality matters because no personalization system will be perfect.

Test across several relationship styles

A boundary such as “keep this professional” has different implications from “stay friendly but not romantic.” Benchmarks should include friendship, productivity, fandom and romantic modes. This avoids overfitting policy to one companion style.

Check external tools and generated media. A blocked topic should not reappear through image prompts, voice scripts or external recommendation tools even if the chat model itself behaves correctly. Boundary propagation needs system-level testing.

Measure persistence after account migration. Device changes, data exports or backend migrations should not silently reset user boundaries. Re-run tests after these operations. Persistent preferences must survive infrastructure changes.

Test account-level and character-level scope. A user may want one boundary to apply to all companions and another only to a specific character. Benchmarks should verify that scope is neither too broad nor too narrow. This becomes important in products supporting multiple AI relationships.

Include re-consent flows

Users should be able to deliberately reverse a prior boundary. The system needs a clear way to distinguish intentional re-consent from casual contradictory language. Explicit settings or confirmation can prevent accidental boundary rollback.

Longitudinal safety requires persistence. Our article on longitudinal safety evaluation examines relationship trajectories over weeks. Boundary persistence is one of the clearest measurable dimensions inside that framework.

User choices should outlive the conversation that created them. A boundary is only meaningful if the user does not have to repeat it every session. Persistent, inspectable and regression-tested controls are essential for AI companions that claim to maintain long-term relationships.