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COMPEL Body of Knowledge
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COMPEL Glossary / replayability

Replayability

The ability to deterministically reproduce an agent trace given captured inputs — a recorded session, the prompt, the tool responses, the random seed.

What this means in practice

Replayability is the foundation for post-incident debugging and for evaluating alternative agent designs against the same historical workload.

Synonyms

agent replay , deterministic agent replay

Related Terms Network

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

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

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
  • 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.
  • Simulation harness — A virtual environment for agent evaluation without production side effects — mock tools, synthetic data, deterministic scenarios.
  • 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.

See also

  • 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.
  • Simulation harness — A virtual environment for agent evaluation without production side effects — mock tools, synthetic data, deterministic scenarios.
  • 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.