OpenTelemetry

Agent stacks mix frameworks, models, and custom services. OTel lets you trace the entire request path in one system instead of siloed vendor dashboards.

OpenTelemetry is USB-C for observability — one plug on your code, many monitors on the other end.

Scenario

OTel trace spans: API gateway → agent orchestrator → OpenAI chat (1.2s) → Postgres tool (80ms) → response, all linked by trace_id.

Solution

In Agent Evaluation & Observability, apply OpenTelemetry to this scenario: OTel trace spans: API gateway → agent orchestrator → OpenAI chat (1. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to OpenTelemetry (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where OpenTelemetry happens in the code.

1from opentelemetry import trace  # import dependencies2from opentelemetry.sdk.trace import TracerProvider  # import dependencies3from opentelemetry.sdk.trace.export import BatchSpanProcessor  # import dependencies4from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter  # import dependencies5
6provider = TracerProvider()7provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))8trace.set_tracer_provider(provider)

• Treating OpenTelemetry as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for opentelemetry

Quick recap — the most important points from this module.