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Map AXPA findings to Trace Parser or DynamicsPerf X++ class/method evidence when normalized trace exports are available.
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Analyze slow AX frontends, problematic users/clients, broad inventory queries, and machine-wide impact without installing client parsers.
Build or run Autonomous Ops workflows for AX Performance Advisor, including investigation queues, evidence acquisition plans, follow-up questions, change drafts, validation planning, and readiness gates.
Generate AI learning and decision-quality artifacts such as recommendation memory, similarity search, acceptance simulation, narrative variants, anomaly explanations, and action confidence tuning.
Generate autonomous AI/USP investigation artifacts including evidence scout, investigation tree, root-cause debate, recommendation quality gate, KPI storyboard, and anonymized pattern library.
Generate guarded admin execution previews with policy gates, confirmation tokens, audit records, and rollback/validation metadata.
Generate advanced operational USP outputs including SLO burn rate, maintenance sequencing, cost of delay, release gates, retention candidates, known issue matching, and executive briefings.
Basado en la clasificación ocupacional SOC
| name | xpp-attribution-mapping |
| description | Map AXPA findings to Trace Parser or DynamicsPerf X++ class/method evidence when normalized trace exports are available. |
Run scripts/xpp_attribution.py. If trace_parser.csv or dynamicsperf_trace.csv is missing, report requires-trace-evidence instead of inventing class/form/report mappings.