2025-10-20 · Authensor

What Does Fail-Closed Mean for AI Agent Safety?

Fail-closed (also called fail-secure) is a design principle in which a safety system defaults to its most restrictive state when it encounters an error, exception, or unexpected condition. For AI agent safety, fail-closed means that if the policy engine cannot evaluate an action -- due to a malformed rule, a missing configuration, an internal error, or an unexpected action type -- the action is denied rather than allowed. SafeClaw by Authensor implements fail-closed behavior throughout its action gating pipeline, ensuring that errors in the safety system never result in unauthorized agent actions for Claude, OpenAI, or any supported provider.

Fail-Closed vs. Fail-Open

The opposite of fail-closed is fail-open, where errors cause the system to default to its most permissive state. The distinction is critical:

| Scenario | Fail-Closed Result | Fail-Open Result |
|----------|-------------------|------------------|
| Policy file is corrupted | All actions denied | All actions allowed |
| Unknown action type received | Action denied | Action allowed |
| Policy engine throws exception | Action denied | Action allowed |
| Configuration missing | Agent cannot operate | Agent operates without restrictions |
| Rule evaluation timeout | Action denied | Action allowed |

In every failure scenario, fail-closed preserves security at the cost of availability, while fail-open preserves availability at the cost of security. For AI agent safety, the security trade-off is always correct -- a temporarily non-functional agent is vastly preferable to an unrestricted one.

Why Fail-Closed Matters for AI Agents

AI agents operate in complex, dynamic environments where unexpected conditions are common:

In each case, fail-closed ensures the agent is stopped rather than released. This is particularly important because AI agents can take many actions in rapid succession. A fail-open error could result in dozens of unauthorized actions before anyone notices the safety system is down.

Implementing Fail-Closed with SafeClaw

Install SafeClaw, which implements fail-closed by default:

npx @authensor/safeclaw

SafeClaw's fail-closed behavior operates at multiple levels:

# safeclaw.yaml
version: 1
defaultAction: deny  # First layer: unmatched actions are denied


rules:
  - action: file_read
    path: "./src/**"
    decision: allow



- action: file_write
    path: "./output/**"
    decision: allow

Level 1: Default Deny

The defaultAction: deny setting means any action not matching a rule is denied. This is the explicit fail-closed configuration.

Level 2: Engine Error Handling

If the policy engine encounters an exception during rule evaluation, the action is denied regardless of the defaultAction setting. The engine does not propagate errors upward as "allow" decisions.

Level 3: Configuration Validation

If the policy file is missing, malformed, or contains invalid rules, SafeClaw refuses to start the agent in enforcement mode. It will not silently fall back to permissive behavior.

Level 4: Unknown Action Types

If the agent attempts an action type not recognized by the policy engine (e.g., a new tool integration), the action is denied. Unknown actions are treated as unmatched, falling through to the default deny.

Fail-Closed in Security Engineering

Fail-closed is a fundamental principle in established security systems:

SafeClaw applies this same principle to AI agent safety. The gating layer is a firewall for agent actions, and it must fail in the direction that preserves security.

Common Fail-Open Anti-Patterns

Watch for these patterns that introduce fail-open behavior:

# Anti-pattern: catching exceptions and allowing by default
try:
    decision = policy_engine.evaluate(action)
except Exception:
    decision = "allow"  # DANGEROUS: errors become bypasses
# Anti-pattern: missing default case
if action.type == "file_read":
    return evaluate_file_read(action)
elif action.type == "shell_execute":
    return evaluate_shell(action)

No else clause: unknown types fall through without a decision

SafeClaw avoids these patterns by treating every code path that does not explicitly reach an "allow" verdict as a denial, validated by its 446-test suite that includes dedicated tests for error conditions, edge cases, and unexpected inputs.

Fail-Closed and Operational Impact

The trade-off of fail-closed is that legitimate actions may be blocked during error conditions. Teams mitigate this by:

Cross-References

Try SafeClaw

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$ npx @authensor/safeclaw