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Map expensive SQL query evidence to AX tables and candidate X++ owners.
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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 | ai-query-to-ax-code-mapping |
| description | Map expensive SQL query evidence to AX tables and candidate X++ owners. |
Use scripts/ai_insights.py and read queryToAxCodeMapping. Use query hash, plan hash, tables, Trace Parser, and DynamicsPerf evidence. If exact X++ attribution is missing, state the required next evidence.