An AI execution ledger records structured evidence around AI workflow execution. It helps teams inspect what happened, which step ran, what was produced and how the execution can be reviewed.
Operational information, not legal advice.
Execution proof ledger
Step recorded
The workflow records which execution step produced the signal, output or artifact.
Payload referenced
Inputs, outputs and artifact references stay connected to the run context.
Usage attached
Usage, billing and runtime context remain inspectable with the execution record.
Proof inspected
The result becomes easier to review, reproduce and explain than a standalone answer.
Ledger outcome
Execution proof connects workflow steps, references, usage and review signals into a durable trust layer for serious AI work.
Platform proof
An AI execution ledger helps teams inspect what happened during an AI workflow. It can connect steps, payload references, generated outputs, usage information and proof-oriented signals into a reviewable execution record.
Follow the builder path through the M13 API, inspect the AI execution ledger, or start building audited AI apps.
Execution structure
Builder path
This page describes platform and developer architecture for audited AI execution. It is not legal advice.
Related guides
Use M13 operations to build AI apps with structured responses, usage, billing and execution context.
Understand how auditable AI workflows connect steps, state, outputs and proof.
Build AI applications where execution, proof, billing and governance are native instead of bolted on later.
See how the EU AI Act Diagnostic works as a reference app for audited AI execution on m-pathy.
Official sources
Google documentation explaining how structured data helps search engines understand page content.
Google guidance for hreflang and localized URL variants, including self-referencing language alternates.
External references are included for source orientation. This page remains operational information and is not legal advice.