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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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Baseado na classificação ocupacional SOC
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.
| 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.