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To audit agentic data-readiness, map raw data sources against processed snapshots and consumer interfaces to identify where agents pull unoptimized data instead of using pre-aggregated snapshots, ensuring high-performance operations.

Agents cause redundant API calls by bypassing the Prep Table to pull raw data directly from the Pantry. This anti-pattern is identified by evaluating data flow efficiency within the Maven CMO-Agent architecture to locate latency bottlenecks.

Data-readiness optimization in an agentic OS data layer is the process of validating data taxonomy integrity and mapping raw inputs to processed snapshots, ensuring agents retrieve pre-aggregated data for efficient operations.

Prioritize data-layer improvements by ranking identified optimization opportunities based on potential session time savings and agent impact, generating a data-readiness report that highlights quick-win performance fixes.

Yes, auditing a Maven CMO-Agent architecture requires access to the capability graph and data taxonomy to validate the integrity of the agentic OS data layer and accurately map data flow efficiency.

You should not use an automated audit when the capability graph or data taxonomy is inaccessible, as validating the integrity of the agentic OS data layer requires these components to accurately map data flow and identify anti-patterns.