What is Distributed Tracing for LLMs?

Distributed tracing for LLMs tracks requests across multi-step AI workflows. As applications move from simple prompt-response patterns to complex agent architectures, understanding the full execution path becomes critical.

Why LLMs Need Distributed Tracing

A modern AI application might:

Retrieve context from a vector database
Call an LLM to generate a plan
Execute multiple tool calls in parallel
Synthesize results with another LLM call
Apply guardrails before returning

Without tracing, when something fails or runs slowly, you're debugging blind.

What is distributed tracing for LLMs?

Distributed tracing for LLMs is the practice of tracking requests across multi-step AI workflows. Each step (LLM call, tool use, retrieval) becomes a span within a trace, enabling end-to-end visibility into complex agent pipelines.

Why do LLM applications need distributed tracing?

Modern AI applications involve chains of operations: retrieval, multiple LLM calls, tool execution, and post-processing. Without tracing, debugging failures or optimizing latency across these steps is nearly impossible.

Trace and Span Hierarchy

LLM tracing follows the OpenTelemetry model:

Trace — The complete request lifecycle, from user input to final response.

Span — A single operation within the trace. Spans can be nested (parent-child relationships).

What span types should LLM tracing capture?

LLM tracing should capture: llm (model inference), retrieval (vector search, RAG), tool (function calls), chain (sequential operations), and agent (autonomous decision loops).

Key Metrics Per Span

Each span should capture:

Duration (latency)
Input and output payloads
Token counts (for LLM spans)
Cost attribution
Error status and messages
Model and provider (for LLM spans)

Implementation with DriftRail

DriftRail's tracing API makes it easy to instrument your AI workflows:

// Create a trace for the request
const trace = await driftrail.createTrace({
  app_id: 'my-agent',
  name: 'customer-support-query'
});

// Add spans for each operation
const retrievalSpan = await driftrail.addSpan(trace.trace_id, {
  name: 'vector-search',
  span_type: 'retrieval'
});

// ... perform retrieval ...

await driftrail.updateSpan(retrievalSpan.span_id, {
  status: 'completed',
  duration_ms: 45
});

The dashboard provides a visual trace viewer showing the full execution tree with timing breakdowns.