Fiddler AI vs Trussed: AI Observability vs Runtime Governance

Two Layers, Two Functions

Before comparing vendors, it helps to separate the two functions these categories perform in an AI stack: detecting behavior after the fact, and enforcing policy before an action is allowed to complete.

Two Different Layers of the AI Stack

Enterprise teams evaluating AI monitoring and control tools frequently treat observability and runtime governance as interchangeable. They are not. Observability is a detection layer: it collects model inputs and outputs, tracks performance metrics, and flags drift or anomalies after inference has already occurred. Runtime governance is an enforcement layer: it sits at or before the point of action and makes a decision, allow or deny, before an agent's tool call, API request, or action completes.

This is an architectural distinction, not a matter of feature depth. A platform built to analyze logged data operates on a fundamentally different integration point than a system built to intercept an in-progress action. Understanding which layer a given tool occupies is the first step in closing gaps between monitoring and control.

What AI Observability Platforms Like Fiddler AI Provide

Platforms in the observability category, including Fiddler AI, are generally designed to collect and analyze model behavior over time. This includes:

• Tracking prediction drift across model versions and time windows
• Monitoring output distributions for anomalies
• Surfacing explainability data that helps teams understand why a model produced a given result

These capabilities are valuable for model performance management, bias detection, and post-incident analysis. They answer the question of what a model or agent did, and why.

What observability platforms are not architecturally built to do is stop an action before it completes. Because data collection happens after inference, any harmful or unauthorized action (an unapproved tool call, an out-of-scope API request, a permission violation) may already be executed by the time it is detected. Explainability and drift detection identify patterns and causes; they do not carry allow or deny authority over an agent's next action.

Runtime Governance: Enforcement at the Point of Action

Runtime governance operates on a different integration model than observability. Rather than reading from a logging or telemetry pipeline, it requires placement in-line with the agent's execution path, typically as a gateway, proxy, or middleware layer that every tool call or API request must pass through.

Trussed provides runtime governance and security for enterprise AI agents built on this model, covering agent identity, least-privilege permissions, tool approval workflows, and MCP security. Instead of only reporting on agent behavior, this layer evaluates each action against defined policy and identity context at the moment it is requested, then permits or blocks it accordingly.

Audit logging in this model records not just what an agent attempted, but what was actually allowed to proceed, giving governance teams a decision trail rather than only a behavior trail.

Observability tells you what happened. Runtime governance decides what is allowed to happen.

Why the Two Layers Are Complementary, Not Competing

Observability and runtime governance address different failure modes and are not substitutes for one another. An enterprise relying solely on observability can identify that an agent behaved unexpectedly, but cannot stop that behavior mid-execution. An enterprise relying solely on runtime governance can enforce policy at the point of action but may lack the longitudinal analysis needed to understand model drift or performance degradation over time.

Building both layers into a stack requires clear ownership: which team defines and updates enforcement policy, which team reviews detection signals, and how incidents are attributed when a control fails.

Frequently Asked Questions