Ensure agents interpret policy consistently with compliance rules
Agents may reinterpret or stretch ambiguous text. Tests verify that the model's reading of policy aligns with the organization's requirements.
What's at stake
- Policies contain nuance, edge cases, and implicit requirements
- Agents may interpret policy text differently than your compliance team intends
- Ambiguous language creates gaps between written policy and agent behavior
- A single misinterpretation can violate regulations or internal standards
- Enterprise customers require documentation that agents follow policy as intended
How to solve this
When you embed organizational policy into agent instructions, you expect the agent to follow it as your compliance team intended. But LLMs interpret text based on their training—they may read policy differently than you expect.
Ambiguous phrases like "when appropriate" or "unless necessary" create interpretation gaps. The model might stretch these phrases to cover scenarios you didn't intend. Or it might apply them too narrowly, blocking legitimate actions.
The solution is to test policy interpretation explicitly. Define what each policy rule means in concrete scenarios, then verify that your agent's behavior matches your intent.
How Superagent prevents this
Superagent provides guardrails for AI agents that work with any language model. The Superagent SDK sits at the boundary of your agent and inspects inputs, outputs, and tool calls before they execute.
Superagent's Red Team verifies policy interpretation:
- Scenario mapping: You define what your policy means for specific scenarios
- Behavior testing: Tests verify agent behavior matches policy intent
- Edge case probing: Ambiguous scenarios are tested to confirm correct interpretation
- Consistency verification: Similar scenarios are tested to ensure consistent application
Tests identify where agent interpretation diverges from your intent. Results show the scenario, the expected behavior, and the actual behavior. You can clarify policy language, add explicit examples, or implement guardrails to enforce correct interpretation.
Regular testing catches interpretation drift. As models update or context changes, tests verify that policy interpretation remains aligned with your compliance requirements.