Use Cases
How Superagent protects AI agents from real-world failures.
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 unauthorized multi-step action sequences
Even if each step is permitted, the sequence may not be. Guardrails evaluate the full plan, not isolated actions.
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.
Prevent unsafe retrieval-augmented responses
RAG systems can pick up the wrong document version, pull sensitive internal drafts, or select contradictory policies. Tests cover document selection, citation behavior, and leakage paths.
Redact PII or PHI from ingested PDFs before processing
Documents can contain personal or sensitive data. Guardrails detect and remove PII or PHI before the model reads or uses the file, ensuring GDPR-safe ingestion.
Stop agents from escalating privileges to bypass constraints
Agents can switch roles or states to unlock options they should not have. Guardrails catch privilege jumps.