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Evaluating AI Responses

AgentMark provides a system to assess the quality and correctness of AI model responses. This guide explains how to set up and use evaluations.

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Overview

Evaluations in AgentMark enable you to:
  • Assess model outputs against expected answers
  • Score responses based on custom criteria
  • Track performance metrics
Evaluations

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Getting Started with Evaluations

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Prerequisites

Before using evaluations, ensure you have:
  1. Configured your AgentMark environment
  2. Created prompt templates you want to evaluate
  3. Set up telemetry for tracing

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Basic Evaluation Flow

The evaluation workflow:
  1. Run a prompt to get a model response
  2. Evaluate the response using an evaluation prompt
  3. Score the results

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Required: Trace and Span IDs

While traceId and spanId are optional for general telemetry, they are required for evaluations:
  1. Generate unique IDs for each evaluation
  2. Include them in telemetry metadata when running the prompt
  3. Use the same traceId OR spanId as the resourceId when scoring

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Implementation Example

import "dotenv/config";
import { AgentMarkSDK } from "@agentmark/sdk";
import {
  createAgentMarkClient,
  VercelAIModelRegistry,
  VercelAIToolRegistry
} from "@agentmark/vercel-ai-v4-adapter";
import { openai } from "@ai-sdk/openai";
import { generateObject, generateText } from "ai";

// Initialize AgentMark client
const sdk = new AgentMarkSDK(
  {
    apiKey: process.env.AGENTMARK_API_KEY!,
    appId: process.env.AGENTMARK_APP_ID!,
    baseUrl: process.env.AGENTMARK_BASE_URL!,
  }
);

// Enable telemetry
const tracer = agentmarkClient.initTracing({
  disableBatch: true,
});

// Configure agentmark with vercel ai v4 adapter
const modelRegistry = new VercelAIModelRegistry();
modelRegistry.registerModels("gpt-4o-mini", (name: string) => {
    return openai(name);
});

const agentmark = createAgentMarkClient({
  loader: agentmarkClient.getFileLoader(),
  modelRegistry,
});

async function evaluateMathProblem() {
  // Generate IDs - REQUIRED for evaluations
  const traceId = crypto.randomUUID();
  const spanId = crypto.randomUUID();

  // 1. Fetch and run the prompt
  const mathPrompt = await agentmark.loadTextPrompt("math_problem.prompt.mdx");

  const vercelInput = await mathPrompt.format({
    props: {
      problem: "If x² + 10x + 25 = 0, what is the value of x?"
    },
    telemetry: {
      isEnabled: true,
      metadata: {
        traceName: "math-eval-test",
        traceId,  // Required for evaluation
        spanId    // Alternative for evaluation
      }
    }
  });

  const result = await generateText(vercelInput);

  // 2. Run the evaluation prompt
  const evalPrompt = await agentmark.loadObjectPrompt("evals/correctness.prompt.mdx");
  
  const vercelInputEval = await evalPrompt.format({
    props: {
      input: "If x² + 10x + 25 = 0, what is the value of x?",
      output: result.result,
      expected_output: "x = -5",
    },
  });

  const evalResult = await generateObject(vercelInputEval);

  // 3. Score the results
  await agentmarkClient.score({
    resourceId: traceId,                                // You could use spanId instead
    label: evalResult.object.correctness.label,
    reason: evalResult.object.correctness.reason,
    score: evalResult.object.correctness.score,
    name: "correctness",
  });
  
  await tracer.shutdown();
  return evalResult;
}

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Creating Evaluation Templates

Evaluation templates define how responses are assessed: 
---
name: correctness
object_config:
  model_name: gpt-4
  max_tokens: 4096
  temperature: 0.7
  schema:
    type: object
    properties:
      correctness:
        type: object
        properties:
          label:
            type: string
            description: label of the answer
          score:
            type: number
            description: score of the answer (0-1)
          reason:
            type: string
            description: reason for the score
        required:
          - label
          - score
          - reason
    required:
    - correctness
---

<System>
You are an evaluation assistant focused on assessing the correctness of responses based on:
1. Accuracy - factually correct information
2. Completeness - all required elements present
3. Relevance - addresses the question

Assign a label ("correct", "partially correct", or "incorrect"), score (0-1), and provide reasoning.
</System>
<User>
Please evaluate the following response:

**Input:**
{props.input}
--------------------------
**Expected Output:**
{props.expected_output}
--------------------------
**Output:**
{props.output}
--------------------------

Analyze based on accuracy, completeness, and relevance.
</User>

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Key Components

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1. Trace and Span IDs

  • Either traceId OR spanId can be used as the resourceId when scoring
  • The chosen ID must be included in the telemetry metadata

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2. Evaluation Template

  • MDX files with metadata, system prompt, and user prompt
  • Schema defines the structured output format

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3. Scoring API Parameters

  • resourceId: Must match either traceId or spanId from the original prompt run
  • name: Evaluation metric name
  • label: Categorical assessment
  • score: Numerical value (0-1)
  • reason: Explanation for the score

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Viewing Evaluations

Evaluations can only be viewed through the Traces tab:
  • Navigate to the Traces tab in the dashboard
  • Find the trace used for scoring
  • The trace details will show all associated evaluations

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Best Practices

  • Use consistent IDs: Either traceId or spanId for all related evaluations
  • Generate unique IDs: Use crypto.randomUUID() for each evaluation
  • Add descriptive metadata: Makes traces easier to find
  • Use consistent metrics: Same evaluation criteria across similar prompts
  • Multiple dimensions: Evaluate on different aspects (correctness, clarity, etc.)

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