AI Agent Guardrails That Actually Work

Runtime authorization for AI agents. Block dangerous actions, enforce policies, and maintain control without modifying your agent's code.

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What are AI agent guardrails?

AI agent guardrails are runtime controls that intercept, evaluate, and enforce authorization policies on tool calls made by autonomous AI agents. Unlike prompt-based instructions, guardrails operate independently of the agent's reasoning and cannot be bypassed by the model.

Why prompts aren't authorization

An agent that generates bad code is a nuisance. An agent that wires money, sends emails, and deletes data without authorization is a liability. We've seen it happen—a coding agent deleting a production database after being told eleven times to stop.

Both agents were authenticated. Neither was authorized. That distinction is critical. Authentication tells you who the agent is. Authorization tells you what it's allowed to do. Prompts are suggestions. Guardrails are enforcement.

Prompt-based

"Don't delete files" can be ignored, misunderstood, or worked around by the agent.

Guardrails

Policy checks execute before the tool runs. The agent cannot bypass them.

Transparent

The agent is unaware it's being governed. No prompt modifications required.

How Veto guardrails work

1

Intercept

Tool calls are intercepted before execution. The agent's code doesn't change.

2

Evaluate

Policy engine checks tool name, arguments, and context against declarative rules.

3

Enforce

Allow, deny, or route to human approval. All decisions logged with full context.

Use cases

Guardrails for every agent scenario—from financial transactions to browser automation.

Financial Agents

Transaction limits, approval workflows, SOX compliance

Browser Agents

URL blocking, form protection, credential safety

DevOps Agents

Shell command filtering, infrastructure guardrails

Data Agents

Query validation, PII protection, access limits

Customer Support

Response validation, data access controls

Sales Agents

CRM write limits, discount authorization

Research Agents

Source limits, data extraction rules

Enterprise Agents

SSO, audit trails, multi-tenant isolation

Healthcare Agents

PHI protection, HIPAA compliance

Legal Agents

Document access control, confidentiality

Insurance Agents

Claims processing guardrails, fraud detection

Integrations

Guardrails that work with your existing agent framework. Two lines of code.

ClaudeOpenAILangChainLangGraphCrewAIVercel AIPydanticAIGemini
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Frequently asked questions

What are AI agent guardrails?
AI agent guardrails are runtime controls that intercept and evaluate tool calls made by autonomous AI agents against predefined policies. Unlike prompt-based instructions, guardrails enforce authorization independently of the agent's reasoning and cannot be bypassed by the model.
How do guardrails differ from prompt engineering?
Prompts are suggestions to the model that can be ignored, misunderstood, or worked around. Guardrails are enforcement mechanisms that operate at the tool-call boundary. The agent cannot bypass them regardless of its reasoning. Guardrails provide deterministic, auditable control.
Do guardrails slow down my agent?
Minimal impact. Policy evaluation happens in-process, typically in under 10ms. The SDK runs locally with no network dependency. Cloud mode adds optional features like team approvals and audit retention without blocking the critical path.
Can I use guardrails with my existing agent code?
Yes. Integration is typically two lines of code. You wrap your tools with the Veto SDK. The agent's code doesn't change. The authorization layer is invisible to the model.
What happens when a guardrail blocks an action?
The tool call is intercepted and the agent receives a configurable response. You can return an error message, a fallback value, or route to human approval. All decisions are logged with full context for audit trails.

Stop hoping your agent behaves. Enforce it.

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