Kategorie: AI & Automation

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AI Observability: Why Your AI Agents Need OpenTelemetry

The Black Box Problem in AI Agents

When you deploy an AI agent in production, you’re essentially running a complex system that makes decisions, calls external APIs, processes data, and interacts with users—all in ways that can be difficult to understand after the fact. Traditional logging tells you that something happened, but not why or how long or at what cost.

For LLM-based systems, this opacity becomes a serious operational challenge:

  • Token costs can spiral without visibility into per-request usage
  • Latency issues hide in the pipeline between prompt and response
  • Tool calls (file reads, API requests, code execution) happen invisibly
  • Context window management affects quality but rarely surfaces in logs

The answer? Observability—specifically, distributed tracing designed for AI workloads.

OpenTelemetry: The Standard not only for AI Observability

OpenTelemetry (OTEL) has emerged as the industry standard for collecting telemetry data—traces, metrics, and logs—from distributed systems. What makes it particularly powerful for AI applications:

Traces Show the Full Picture

A single user message to an AI agent might trigger:

  1. Webhook reception from Telegram/Slack
  2. Session state lookup
  3. Context assembly (system prompt + history + tools)
  4. LLM API call to Anthropic/OpenAI
  5. Tool execution (file read, web search, code run)
  6. Response streaming back to user

With OTEL traces, each step becomes a span with timing, attributes, and relationships. You can see exactly where time is spent and where failures occur.

Metrics for Cost Control

OTEL metrics give you counters and histograms for:

  • tokens.input / tokens.output per request
  • cost.usd aggregated by model, channel, or user
  • run.duration_ms to track response latency
  • context.tokens to monitor context window usage

This transforms AI spend from „we used $X this month“ to „user Y’s workflow Z costs $0.12 per run.“

Practical Setup: OpenClaw + Jaeger

At it-stud.io, we tested OpenClaw as our AI agent framework – already supporting OTEL by default – and enabled full observability with a simple configuration change:

{
  "plugins": {
    "allow": ["diagnostics-otel"],
    "entries": {
      "diagnostics-otel": { "enabled": true }
    }
  },
  "diagnostics": {
    "enabled": true,
    "otel": {
      "enabled": true,
      "endpoint": "http://localhost:4318",
      "serviceName": "openclaw-gateway",
      "traces": true,
      "metrics": true,
      "sampleRate": 1.0
    }
  }
}

For the backend, we chose Jaeger—a CNCF-graduated project that provides:

  • OTLP ingestion (HTTP on port 4318)
  • Trace storage and search
  • Clean web UI for exploration
  • Zero external dependencies (all-in-one binary)

What You See: Real Traces from AI Operations

Once enabled, every AI interaction generates rich telemetry:

openclaw.model.usage

  • Provider, model name, channel
  • Input/output/cache tokens
  • Cost in USD
  • Duration in milliseconds
  • Session and run identifiers

openclaw.message.processed

  • Message lifecycle from queue to response
  • Outcome (success/error/timeout)
  • Chat and user context

openclaw.webhook.processed

  • Inbound webhook handling per channel
  • Processing duration
  • Error tracking

From Tracing to AI Governance

Observability isn’t just about debugging—it’s the foundation for:

Cost Allocation

Attribute AI spend to specific projects, users, or workflows. Essential for enterprise deployments where multiple teams share infrastructure.

Compliance & Auditing

Traces provide an immutable record of what the AI did, when, and why. Critical for regulated industries and internal governance.

Performance Optimization

Identify slow tool calls, optimize prompt templates, right-size model selection based on actual latency requirements.

Capacity Planning

Metrics trends inform scaling decisions and budget forecasting.

Getting Started

If you’re running AI agents in production without observability, you’re flying blind. The good news: implementing OTEL is straightforward with modern frameworks.

Our recommended stack:

  • Instrumentation: Framework-native (OpenClaw, LangChain, etc.) or OpenLLMetry
  • Collection: OTEL Collector or direct OTLP export
  • Backend: Jaeger (simple), Grafana Tempo (scalable), or Langfuse (LLM-specific)

The investment is minimal; the visibility is transformative.

At it-stud.io, we help organizations build observable, governable AI systems. Interested in implementing AI observability for your team? Get in touch.