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AgentMark provides built-in observability powered by OpenTelemetry, allowing you to track, monitor, and debug your prompts throughout their lifecycle.

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What Gets Tracked

AgentMark automatically collects: Inference Spans - Full prompt execution lifecycle
  • Token usage and costs
  • Response times
  • Model information
  • Completion status
Tool Calls - When prompts use tools
  • Tool name and parameters
  • Execution duration
  • Success/failure status
  • Return values
Streaming Metrics - For streaming responses
  • Time to first token
  • Tokens per second
  • Total streaming duration
Span Kinds - Categorize operations by type
  • LLM, tool, agent, retrieval, embedding, guardrail, and function
  • Used for filtering, graph visualization, and analytics grouping
  • Set via observe() or ctx.span()
Sessions - Group related traces
  • Organize by user interaction
  • Track multi-step workflows
  • Monitor batch processing
  • Analyze performance patterns

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Quick Start

Enable telemetry when formatting your prompts: 
import { client } from './agentmark.client';
import { generateText } from 'ai';

const prompt = await client.loadTextPrompt('greeting.prompt.mdx');
const input = await prompt.format({
  props: { name: 'Alice' },
  telemetry: {
    isEnabled: true,
    functionId: 'greeting-handler',
    metadata: {
      userId: 'user-123',
      sessionId: 'session-abc',
      sessionName: 'Customer Support Chat'
    }
  }
});

const result = await generateText(input);

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Setup

Initialize tracing in your application: 
import { AgentMarkSDK } from "@agentmark-ai/sdk";

const sdk = new AgentMarkSDK({
  apiKey: process.env.AGENTMARK_API_KEY,
  appId: process.env.AGENTMARK_APP_ID,
  baseUrl: process.env.AGENTMARK_BASE_URL  // defaults to https://api.agentmark.co
});

// Initialize tracing
const tracer = sdk.initTracing();

// Your application code...

// Shutdown tracer (for short-running scripts)
await tracer.shutdown();
For local development with agentmark dev, traces are sent to http://localhost:9418 automatically. Set disableBatch if you need traces flushed immediately (useful for short scripts):
const tracer = sdk.initTracing({ disableBatch: true });

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When to Use

Development:
  • Debug prompt behavior
  • Optimize token usage
  • Understand execution flow
  • Test different approaches
Production:
  • Monitor performance
  • Track costs
  • Debug user issues
  • Analyze usage patterns

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PII masking

If your traces contain sensitive data (emails, SSNs, credit card numbers), you can redact it before it leaves your application. Masking runs client-side in your process — no unmasked data is ever exported. 
import { AgentMarkSDK, createPiiMasker } from '@agentmark-ai/sdk';

const sdk = new AgentMarkSDK({
  apiKey: process.env.AGENTMARK_API_KEY!,
  appId: process.env.AGENTMARK_APP_ID!,
  mask: createPiiMasker({ email: true, ssn: true }),
});
sdk.initTracing();
For a zero-code option, set AGENTMARK_HIDE_INPUTS=true or AGENTMARK_HIDE_OUTPUTS=true to suppress all input or output attributes. Learn more about PII masking

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Next Steps

Traces and Logs

Track execution and debug issues

Sessions

Group related traces together

PII Masking

Redact sensitive data from traces before export