Why Fail-Safe by Default Changes Everything

When we designed AIPC, we faced a decision that would define the entire protocol's credibility with regulated industries: what should happen when an AI agent cannot comply with a data provider's presentation requirements?

The answer we chose — suppress the data entirely — is the single most important design decision in the specification. It is also the most counterintuitive. Here is why it changes everything.

The Default Matters More Than You Think

In any protocol, the default behavior is the behavior that will be most common in practice. Developers rarely override defaults. Systems rarely reconfigure them. Whatever you set as the baseline is what 90% of deployments will use.

For AIPC, the fail_behavior field controls what happens when an AI runtime cannot satisfy a contract's requirements. The options are:

• suppress — Do not present the data at all. The AI responds as if the data does not exist.
• warn — Present the data but include a prominent warning that compliance requirements were not fully met.
• partial — Present whatever portions of the data can be shown compliantly, omitting the rest.
• log — Present the data without restrictions but log the compliance failure for audit purposes.

We set suppress as the default. Not warn. Not partial. Not log. Suppress.

The Alternative Is Worse

Imagine the alternative. A financial data API returns mutual fund performance data with a contract requiring the disclosure "Past performance is not indicative of future results." The AI agent, for whatever reason, fails to include the disclosure. Maybe the context window was too constrained. Maybe the agent's output format did not accommodate it. Maybe a bug in the runtime skipped the disclosure injection step.

Under a warn default, the user sees the performance data with a vague warning that "some compliance requirements may not be fully reflected." The user ignores the warning — users always ignore warnings — and makes an investment decision based on past performance data presented without the legally required disclaimer.

Under a log default, the user sees the performance data with no indication whatsoever that anything is wrong. Somewhere in a server log, a compliance event is recorded. Nobody reads it until the lawsuit.

Under a suppress default, the user sees nothing. The AI says something like "I'm unable to provide that information right now." The user is mildly frustrated. Nobody is harmed. Nobody is misled. Nobody is exposed to liability.

The calculus is simple: a moment of user frustration is infinitely preferable to a compliance violation that could result in regulatory action, lawsuits, or harm to the end user.

Why Compliance Teams Care

We have spoken with compliance officers at financial institutions, healthcare organizations, and legal tech companies. The reaction to fail-safe-by-default is consistently the same: relief.

Every compliance team we have talked to has the same fundamental concern about AI: "What happens when it gets it wrong?" They do not need AI to be perfect. They need to know that when AI fails, it fails safely. suppress gives them that guarantee.

This is the difference between a protocol that compliance teams will block and one they will champion. If the default failure mode is "show data without required disclosures," no compliance team at a regulated institution will approve the integration. If the default failure mode is "show nothing rather than show something improperly," the risk profile changes entirely.

Fail-safe by default transforms the conversation from "prove the AI will always get it right" to "even when the AI gets it wrong, no one is harmed." The first conversation is impossible to win. The second is straightforward.

The Hierarchy

While suppress is the default, AIPC supports a full hierarchy of fail behaviors because different data has different risk profiles. A weather forecast does not need the same fail-safe strictness as a drug interaction warning.

The hierarchy, from most conservative to least:

1. suppress — Nuclear option. No data shown. Appropriate for financial disclosures, medical warnings, legal disclaimers. Anything where showing the data without its required context could cause real harm.
2. warn — Data shown with a compliance warning. Appropriate for situations where the data is still useful but the user should know that presentation requirements were not fully met.
3. partial — Compliant portions shown, non-compliant portions omitted. Useful for composite data where some fields can be shown safely even if others cannot.
4. log — Data shown freely, failure logged. Appropriate for low-risk data where audit trails matter more than real-time enforcement. Product descriptions, general reference data, non-sensitive metrics.

The key insight is that the data provider chooses the appropriate level. They know their data, their regulatory environment, and their risk tolerance. AIPC gives them the controls; the default simply ensures that if they do not explicitly choose, the safest option wins.

A Design Philosophy

Fail-safe by default reflects a broader design philosophy in AIPC: when in doubt, protect the end user. The protocol is not designed to maximize the amount of data AI agents can present. It is designed to maximize the amount of data AI agents can present correctly.

This is a subtle but critical distinction. A protocol optimized for data throughput would default to log — show everything, sort out compliance later. A protocol optimized for trust defaults to suppress — show nothing unless you can show it right.

We chose trust. Regulated industries are built on trust. If AIPC is going to become the standard for how AI presents regulated data, it has to earn that trust from day one. And the best way to earn trust is to demonstrate that you have already thought about what happens when things go wrong.

Things will go wrong. With suppress as the default, when they do, nobody gets hurt.