Guardrails & Safety
Exogram vs Guardrails AI
“Output filtering is not execution governance.”
What Guardrails AI Does
- •Guardrails AI validates and corrects LLM outputs using validators.
- •Checks formatting, content safety, and structural correctness after the model generates a response.
- •Uses LLM-based classification to evaluate outputs — probabilistic, not deterministic.
- •Operates between model and user, not between agent and tool.
What Exogram Does
- ▸Exogram governs before execution — at the boundary between the agent and the tool.
- ▸Uses deterministic logic gates, not LLM-based classification. Same input → same output → every time.
- ▸0.07ms evaluation vs. 50-200ms for LLM-based validators. 100x faster.
- ▸Zero false negatives. LLM-based classification has inherent error rates.
Key Differences
| Dimension | Guardrails AI | Exogram |
|---|---|---|
| What It Validates | Model outputs | Proposed actions |
| Where It Sits | Model → User | Agent → Tool |
| Decision Method | LLM-based (probabilistic) | Code-based (deterministic) |
| Evaluation Speed | 50-200ms | 0.07ms |
| False Negative Rate | Inherent error rate | 0.00% |
The Verdict
Use Guardrails AI if you need output formatting. Use Exogram if you need execution governance. Filtering a response is not the same as blocking a database write.
Frequently Asked Questions
Can I use both Guardrails AI and Exogram?
Yes. Guardrails AI filters outputs (content safety). Exogram governs execution (action safety). They protect different surfaces. Use both for defense in depth.
Why is deterministic enforcement better than LLM-based validation?
LLM-based validation has inherent error rates — the validator itself can hallucinate. Deterministic enforcement uses code logic: if the action matches a blocked pattern, it's blocked. No probability. No error rate. Same input → same output → every time.