+What guardrails libraries are
Guardrails libraries are code you add to an application to validate prompts and outputs — schemas, classifiers, topical rails — inside that app's process.
+Side by side
Capability by capability
| Capability | Guardrails libraries | Intertrace |
|---|---|---|
Validate prompts and outputs | ✓ | ✓ |
Enforced outside the app's own process A manipulated agent can't talk its way past a check it doesn't run. | — | ✓ |
One policy across every agent and team | Partial | ✓ |
Covers third-party and coding agents you didn't build | — | ✓ |
Pre-action authorization with permits | — | ✓ |
Discovery of AI no one registered | — | ✓ |
Central evidence and review queue | — | ✓ |
Runs with zero network hop Intertrace adds an edge hop; in-process hooks exist for some runtimes. | ✓ | Partial |
“Guardrails libraries” describes the typical category, not a specific product. Individual products vary.
+Honest answer
Which do you need?
+Guardrails libraries alone is enough when
- You own one or two AI apps and can change their code
- Checks are about output format and topic, not actions
- Each team is happy to maintain its own rules
+You need a decision layer when
- You run many agents across teams, vendors and coding tools
- You need one policy enforced consistently, outside the app
- The agent itself could be manipulated into skipping its own checks
+Better together
Keep the library for app-specific output checks. Put Intertrace in the path for everything that needs a consistent, tamper-resistant decision.
+Other comparisons
+ The AI watching your AI
Run the comparison on your traffic.
Bring your own prompts and tool calls. We'll show what each approach decides.