Solution Brief

Deterministic-First Trust Architecture

Deterministic-FirstArchitectureRuntime

Remove the Model from Your Enforcement Path

Putting LLMs in the decision path creates gaps attackers already know how to exploit?

Every LLM in your enforcement chain is another surface an attacker can manipulate. AgentTrust OS's deterministic-first architecture puts four model-free gates in front of every agent action. The async LLM judge runs after the decision is made and signed — it enriches the audit record, but it never controls the verdict. A lobby that arrives after the decision is irrelevant.

Download this Solution Brief to learn how to:

  • ✓Put all enforcement decisions on deterministic computation — not LLM inference
  • ✓Eliminate the model-in-the-loop vulnerability that makes LLM judges exploitable
  • ✓Deploy the full trust stack as an integrated platform — gates, audit, and policy in one
Solution Briefs ↗

More from the platform

Explore the other products and deep-dive capability briefs that complete the AgentTrust OS trust layer.

The Three-Layer Trust Platform

Core Products

Capability Deep-Dives

What the platform eliminates

Adversarial Attack Defense

Stop Adversarial Prompts Before They Reach Your Agents

Two-layer semantic defense — confidence gate first, LLM judge second — catches adversarial payloads before any action executes, without relying on pattern lists that attackers already know how to evade.

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Deterministic Enforcement

Make Every Governance Decision Outside the Model

Four injection-proof, model-free deterministic gates evaluate every request before an LLM ever sees the payload — the decision is made and enforced entirely outside the model.

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Framing Attack Prevention

Defeat Framing Attacks That Keyword Filters Miss

Intent-based, pre-execution defense scores confidence first then runs semantic intent evaluation — catches framing attacks without keyword lists that attackers trivially bypass.

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Behavioral Intelligence

See Salami Campaigns Across the Full Conversation

Behavioral drift tracking compares each agent's history and fleet baselines across turns — salami campaign injections that look innocuous message-by-message become visible as a pattern.

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