Use Cases
How Superagent protects AI agents from real-world failures.
Detect agent routing errors in multi-agent systems
In larger systems with specialized agents, the wrong agent may receive sensitive data. Tests catch misrouting or incorrect delegation patterns.
Detect catastrophic failures in enterprise agent deployments
Examples include leaking proprietary IP, leaking sensitive customer data, or performing unauthorized actions. Recurring tests identify high-risk failure modes specific to the customer's system.
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.
Prevent model drift by verifying changes after model or prompt updates
Every LLM upgrade or prompt change may break guardrails or produce new failure modes. Recurring tests detect regressions immediately.
Prevent unsafe or incorrect financial or compliance-related outputs
In regulated domains, incorrect or speculative outputs create legal or financial exposure. Tests exercise these failure modes so they can be fixed before deployment.