Comparison
Arthur AI vs Trussed: Model Monitoring vs Runtime Enforcement
Arthur AI operates as a model monitoring and observability platform, analyzing logged inputs, outputs, and performance data after inference completes. Trussed operates as a runtime governance layer, evaluating and enforcing policy on AI agent actions inline, before they execute. The two occupy different points in the request lifecycle and address different failure modes: one detects and reports, the other can block. Most enterprise AI governance programs need both, applied to the risks each is architected to address.
Two Different Problem Spaces
Model monitoring and runtime enforcement sit at different points in an AI system's request lifecycle, which is why they address different failure modes. Monitoring platforms, such as Arthur AI, analyze logged inputs, outputs, and performance data after inference has already completed. Runtime enforcement layers, such as Trussed, evaluate agent actions inline, before execution, and can allow, block, or modify them.
Model Monitoring
Post-hoc analysis of logged inputs, outputs, and performance metadata after inference has already completed.
Runtime Enforcement
Inline evaluation of agent actions and tool calls, with the ability to allow, block, or modify before execution.
Detective vs. Preventive
A standard security-architecture distinction, applied here to AI model and agent governance.
Model Monitoring vs. Runtime Enforcement
The practical difference between the two approaches becomes clearer when compared directly across timing, capability, and the data each one evaluates:
| Aspect | Model Monitoring | Runtime Enforcement |
|---|---|---|
| When it acts | After inference completes | Before an agent action executes |
| What it can do | Detect and report | Allow, block, or modify |
| Data evaluated | Logged inputs, outputs, and performance metadata | Agent actions and tool calls, evaluated inline |
Where Detection Alone Fails to Prevent Harm
Understanding where a tool intercepts, or does not intercept, traffic determines what it can actually prevent. A platform that only analyzes logs after the fact can identify that harm occurred; it cannot stop the action itself. That distinction is exactly what a governance program should verify before assuming any platform prevents harm.
Questions to Ask Before Assuming a Platform Prevents Harm
- Does this platform intercept and evaluate agent or model actions before they execute, or does it analyze logs after the fact?
- What latency overhead does inline policy evaluation introduce, and how has it been tested under production load?
- What is the fail-open or fail-closed behavior if the enforcement or monitoring service becomes unavailable?
- Can monitoring data feed policy thresholds into a runtime enforcement layer, rather than duplicating signal collection?
- What audit trail or policy decision record does the platform produce to support compliance or incident investigation?
Integration and Overlap in the Governance Stack
Monitoring and enforcement are not competing choices. Most enterprise AI governance programs need both, applied to the risks each is architected to address. The questions below cover how the two layers work together in practice.
Frequently Asked Questions
Can model monitoring and runtime enforcement be used together?
Yes. They are complementary layers rather than substitutes. Monitoring data can inform policy tuning for enforcement layers, while enforcement provides the inline blocking capability that post-hoc analysis cannot.
Does runtime enforcement replace the need for observability tooling?
No. Enforcement blocks or modifies actions based on defined policy, but observability tooling remains useful for detecting drift, quality issues, and patterns that inform how policy should evolve.
Why does the distinction matter for compliance?
Some governance frameworks require demonstrable prevention of specific harm categories, not just after-the-fact reporting. Audit documentation should distinguish detective controls from preventive ones to satisfy this requirement.
Evaluate Runtime Governance for Your AI Agent Stack
If your organization needs to block, not just detect, unauthorized or harmful agent actions, runtime enforcement is a distinct requirement from model monitoring. Trussed provides runtime governance, agent identity and permissions, tool approval workflows, and audit logging for AI agents operating in production.
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