Hekate Gate vs LLM guardrails

LLM guardrails help shape model behavior. Hekate Gate makes a separate security decision before an agent uses tools, data, payments, signer paths, or production workflows.

Dimension	Hekate Gate	LLM guardrails
Primary question	Should this agent action be allowed, warned, blocked, or approved by a human?	Did the model output or plan violate a model-facing rule?
Decision path	Deterministic policy checks plus evidence, receipts, and audit events.	Prompt, classifier, LLM-as-judge, or runtime instruction.
Best security boundary	Tool calls, scopes, payments, signer isolation, receipt integrity, and deployment approval.	Content safety, style, refusal behavior, and model-level instruction following.
Audit artifact	Signed security receipt with subject hash, policy hash, evidence hash, scanner version, and issuer.	Trace, model response, classifier score, or prompt log.
Failure mode	Fail closed on critical findings, missing evidence, or invalid receipt.	May be bypassed by prompt injection, tool-result poisoning, or stochastic judgment.
Production role	Approval layer before agents touch real systems.	Helpful model behavior layer inside the agent runtime.

Takeaway

Use guardrails for model behavior. Use Hekate Gate for production action approval. The safest architecture keeps them separate: agent proposes, policy decides, signer signs, verifier validates, ledger records.

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