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COMPEL Glossary / ai-trace

AI trace

A span hierarchy — client → orchestration → retrieval → model → tool — capturing a single AI request end-to-end, including prompts, responses, tool calls, and token usage.

What this means in practice

The AI trace is the unit of debuggability, audit, and incident reconstruction; OpenTelemetry semantic conventions for GenAI standardise its shape.

Synonyms

GenAI trace , LLM request trace

Related Terms Network

This diagram shows how AI trace connects to other concepts in the COMPEL body of knowledge. Each linked node is a term you can explore in turn.

Related terms for AI trace Network graph showing 3 related glossary terms connected to AI trace. AI trace Observabil… Agent trac… Replayabil…
3 related concepts connected to AI trace.

The COMPEL Glossary Graph visualizes relationships between framework terminology, showing how concepts interconnect across domains, stages, and pillars. Term nodes cluster by pillar affiliation while cross-references reveal semantic dependencies — for example, how risk appetite connects to control effectiveness, model governance, and assurance requirements. This network representation helps practitioners navigate the framework vocabulary and understand that COMPEL terminology forms a coherent conceptual system rather than isolated definitions.

Accessible list of related terms
  • Observability for AI — The set of telemetry — prompt/response capture, retrieval traces, tool-call records, token-cost metrics, and evaluation signals — that makes an AI system operable and auditable.
  • Agent trace — A span hierarchy that captures a multi-step agent execution — loop iterations, tool calls, memory reads and writes — with enough fidelity to reconstruct the agent's full decision path.
  • Replayability — The ability to deterministically reproduce an agent trace given captured inputs — a recorded session, the prompt, the tool responses, the random seed.

See also

  • Observability for AI — The set of telemetry — prompt/response capture, retrieval traces, tool-call records, token-cost metrics, and evaluation signals — that makes an AI system operable and auditable.
  • Agent trace — A span hierarchy that captures a multi-step agent execution — loop iterations, tool calls, memory reads and writes — with enough fidelity to reconstruct the agent's full decision path.
  • Replayability — The ability to deterministically reproduce an agent trace given captured inputs — a recorded session, the prompt, the tool responses, the random seed.