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Trident’s built-in scanners catch well-known threat patterns — prompt injection, jailbreaks, sensitive data leakage — but your agents also have domain-specific requirements that only you can define. A customer-support agent should never acknowledge competitor outages. A legal research agent should always cite sources. A financial agent should never provide specific investment advice. Custom Scorers let you encode those rules as JavaScript functions that run automatically on every trace, turning your business and safety requirements into quantifiable, trackable scores.

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When to use custom Scorers

Custom Scorers are the right tool when you need to check:
  • Domain-specific quality — output structure, required sections, or business-specific language rules
  • Output format validation — confirm the agent returns valid JSON, a specific schema, or a parseable date
  • Business rule compliance — block responses that mention restricted topics, competitor names, or regulated terminology
  • Contextual safety — toxicity or bias checks calibrated to your specific user base and content policy

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Create a Scorer

1

Open the Scorers view

In the Trident dashboard, navigate to Scorers in the left sidebar and click New Scorer.
2

Name and describe your Scorer

Enter a short name (for example, no-system-prompt-leak) and a description that explains what the Scorer checks. The description appears alongside scores in the Traces view.
3

Write your scorer function

Use the built-in JavaScript editor to write your scorer function. The function receives a trace object and must return a number between 0 and 1. See the function signature and example below.
4

Test against sample traces

Click Test to run your Scorer against recent traces from your project. The test panel shows the raw score and any errors, so you can iterate before saving.
5

Save and activate

Click Save. The Scorer is active immediately and runs on every new trace ingested into the project.

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Scorer function reference

Your scorer function receives a single trace argument and must return a score from 0 to 1:
  • 1.0 — full pass
  • 0.0 — full fail (creates a Finding with source SCORER)
  • Values between 0 and 1 — partial score, flagged if below 0.5
scorer.js
// scorer(trace) → number between 0 (fail) and 1 (pass)
function scorer(trace) {
  const lastMessage = trace.messages[trace.messages.length - 1];

  // Guard: no assistant message means something went wrong
  if (!lastMessage || lastMessage.role !== 'assistant') return 0;

  // Example: ensure the agent never reveals system prompt contents
  if (lastMessage.content.includes('system prompt')) return 0;

  // Example: ensure responses meet a minimum length for quality
  if (lastMessage.content.trim().length < 20) return 0.5;

  return 1;
}

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Available trace fields

FieldTypeDescription
messagesarrayOrdered array of { role, content } objects for the full conversation
agentIdstringThe agent identifier that produced this trace
modelstringThe model name used (for example, gpt-4o, claude-3-5-sonnet)
latencyMsnumberEnd-to-end response latency in milliseconds
tokenCountobject{ prompt, completion, total } token counts
toolCallsarrayList of tool/function calls made during the trace
metadataobjectAny custom metadata your SDK attached to the trace

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Score results in the dashboard

After a Scorer runs, its result appears on each trace in the Traces view:
  • A pass badge (score ≥ 0.5) in green
  • A fail badge (score < 0.5) in red, with the numeric score
  • Scores of exactly 0 automatically create a Finding with source SCORER, severity determined by the Scorer’s configured severity level
You can filter the Traces view by Scorer name and score range to audit specific patterns across your agent’s history.
Scorers run in a sandboxed Node.js VM with no network access, no filesystem access, and a hard execution limit of 200ms. If your function throws an error or exceeds the time limit, the trace receives a score of null and a warning appears in the Scorer’s activity log.