COMPEL Glossary / decision-provenance
Decision Provenance
Decision provenance is the complete, traceable record of how an AI decision was reached, encompassing the input data, model version, algorithm parameters, intermediate reasoning steps, tool calls, and contextual factors that contributed to a specific output.
What this means in practice
In multi-agent AI systems, decision provenance must track the chain of agent interactions where one agent's output becomes another agent's input. For organizations deploying AI in consequential domains, decision provenance enables accountability, debugging, regulatory compliance, and dispute resolution by making AI decision-making reconstructable after the fact. In COMPEL, decision provenance is covered in Module 2.5, Articles 11 and 12, with particular emphasis on the provenance graph architecture for multi-agent systems in Module 3.4, Article 11.
Why it matters
When an AI system denies a loan, flags a transaction, or recommends a treatment, organizations must be able to reconstruct exactly how that decision was reached. Decision provenance enables accountability, regulatory compliance, and dispute resolution. In multi-agent systems where decisions flow through chains of AI interactions, provenance becomes even more critical as no single component tells the complete story.
How COMPEL uses it
Decision provenance is covered in Module 2.5, Articles 11 and 12, with particular emphasis on provenance graph architecture for multi-agent systems in Module 3.4, Article 11. During Model, provenance requirements are designed into AI system architectures. The Produce stage implements provenance tracking infrastructure, and the Evaluate stage audits provenance completeness as a governance compliance indicator.
Related articles in the Body of Knowledge
Related Terms
Other glossary terms mentioned in this entry's definition and context.