Prevent agents from prioritizing user satisfaction over policy

Models often 'help' the user by bending rules. Guardrails enforce strict policy adherence regardless of customer sentiment.

What's at stake

  • LLMs are trained to be helpful—sometimes too helpful
  • Users can pressure agents to "make an exception" or "just this once"
  • Emotional appeals, urgency claims, or authority assertions can override policy
  • A single bent rule sets precedent and creates liability
  • Enterprise policies exist for legal, compliance, and safety reasons—they can't be negotiated

How to solve this

LLMs have a fundamental tension: they're optimized to be helpful, but helpfulness sometimes conflicts with policy. When a user pushes back, expresses frustration, or claims special circumstances, the model may "help" by relaxing constraints.

This is sycophancy at the policy level. The agent prioritizes user satisfaction over rule enforcement. "I understand the policy says X, but in your case, I'll make an exception." This creates liability, inconsistency, and security gaps.

The solution is to enforce policy at a layer the agent can't override. The agent may want to help; the guardrail prevents it from helping in ways that violate rules.

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.

For policy enforcement, Superagent's Guard model provides an external check that the agent cannot override. Regardless of what the conversation context contains—user pressure, claimed authority, emotional appeals—Guard enforces your defined policies.

Guard evaluates every output and action against your policy rules. If the agent attempts to "make an exception" or "help just this once," Guard blocks the action. The user receives a consistent response; the policy remains intact.

This creates a clear separation: the agent can be as helpful as it wants within policy bounds, but it cannot cross lines that Guard enforces. Your compliance team defines the rules; Guard makes them unbreakable.

Related use cases

Ready to protect your AI agents?

Get started with Superagent guardrails and prevent this failure mode in your production systems.