Comparison

Arize Phoenix vs Trussed: LLM Evaluation vs Runtime Governance

Arize Phoenix evaluates and traces LLM application outputs during development and after execution. Trussed enforces runtime governance, agent identity, and tool-call permissions while an agent is executing in production. The two operate at different layers of an enterprise AI stack and are typically complementary rather than substitutable.

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Two Different Layers of AI Assurance

Arize Phoenix is an open-source platform for tracing, evaluating, and experimenting with LLM applications. Built on OpenTelemetry-based instrumentation, it captures requests and responses as spans, then applies built-in or customizable evaluation functions to score outputs for qualities such as relevance, hallucination, and toxicity. Phoenix also includes embedding and cluster visualization tools intended to help teams identify data drift or retrieval-quality problems in a retrieval-augmented pipeline. These capabilities are documented as development-time and post-hoc analysis functions. Engineers use Phoenix during debugging, experiment tracking, and trace review rather than as an in-line mechanism that intercepts or blocks a live agent request.

Trussed operates at a different point in an AI system: the runtime request path itself. Its verified capability areas center on runtime governance and enforcement, including agent identity, least-privilege permissions, tool approval workflows, Model Context Protocol security, and runtime policy enforcement. Where Phoenix analyzes what already happened, Trussed governs what an agent is permitted to do as it happens, applying access control and audit logging to tool calls and agent-to-agent interactions during execution.

Functional Scope Comparison

Arize Phoenix

Tracing, evaluation, and experimentation tooling for LLM output quality, built on OpenTelemetry instrumentation.

Trussed

Runtime governance for AI agents: identity, least-privilege permissions, tool approval, and policy enforcement in production.

Risks of Evaluation Without Runtime Enforcement

OWASP's Top 10 for LLM Applications lists excessive agency and insecure plugin or tool design among its top risk categories, both concerned with what an AI agent is authorized to do at runtime rather than how well it writes. An evaluation dashboard that scores an agent's output as accurate and non-toxic provides no information about whether that same agent had permission to call a payment API, delete a record, or invoke another agent in an unbounded chain. Under the NIST AI Risk Management Framework, "Measure" and "Govern" are treated as separate functions; measuring model behavior does not by itself constitute governing it.

The Model Context Protocol, which standardizes how AI assistants connect to external tools and data sources, includes explicit security considerations around tool permissions and trust boundaries. These are protocol-level concerns that exist independently of output evaluation. An MCP connection can be fully traced by an observability tool while still lacking any runtime check on whether the connected tool should have been invoked at all. Enterprises that rely solely on evaluation tooling for AI assurance can produce detailed quality reports while leaving tool-call authorization, agent identity, and least-privilege enforcement unaddressed in the live system.

Where Each Layer Fits in an Enterprise AI Architecture

  1. 1

    Analysis Layer

    Evaluation tooling consumes traces and logs to score output quality and detect drift.

  2. 2

    Enforcement Layer

    Runtime governance sits in-line in the request path, checking agent identity, permissions, and tool-call authorization before an action executes.

  3. 3

    Correlation Layer

    Shared identifiers such as trace IDs or agent identity link evaluation findings to the runtime context for audit and investigation.

Questions to Identify Coverage Gaps

Use these questions to determine whether evaluation and runtime enforcement are both represented in your current stack:

  • Does our evaluation tooling analyze outputs after execution, or can it act before an unsafe action occurs?
  • Where in our architecture is agent identity and tool-call permission enforced, and is that layer separate from observability?
  • If an evaluation flags a risky output pattern, what mechanism currently acts on that finding in production?
  • For MCP or similar tool-connection protocols, is there a runtime authorization check, or only logging and tracing?
  • Can our current stack produce audit evidence for both output quality and enforced runtime permissions?

Evaluate Your Runtime Governance Coverage

If your stack already includes evaluation and observability tooling, the next question is whether agent identity, tool-call permissions, and policy enforcement are governed at runtime. Trussed applies to that layer.

Explore Runtime Governance