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Explain AXPA findings for technical teams, AX key users, and management.
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Basado en la clasificación 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-finding-explainer |
| description | Explain AXPA findings for technical teams, AX key users, and management. |
Use scripts/ai_insights.py and read findingExplainers. Present three layers: technical cause, AX/business meaning, and management risk. Include severity, confidence, approval path, and validation evidence.