AX2012_Performance_Advisory

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.

Prioritize AXPA findings using cost-of-delay risk points and deferred-risk reasoning.

Generate AXPA enterprise observability outputs such as time-series store, alerts, estate inventory, plan repository, notification payloads, and competitive coverage.

Plan high-value missing evidence requests such as Trace Parser, DynamicsPerf, deadlocks, batch history, and user sessions.

Generate governance artifacts such as runbooks, RACI, business impact timeline, suppression governance, data quality checks, and audit exports.

Export or inspect the AXPA knowledge graph of findings, playbooks, modules, and tables.

Match AXPA findings against the local known issue pattern library and return fix paths and validation criteria.

Prepare and validate LLM connector payloads for AXPA evidence-grounded Q&A without exposing secrets or making unapproved external calls.

Build local evidence Q&A artifacts with source finding IDs and no external LLM dependency.

Propose maintenance-window sequencing from AXPA findings and playbook groups.

Generate market differentiation outputs such as vendor-neutral comparison, migration readiness, resilience score, knowledge graph, process owner scorecards, evidence marketplace, and value realization.

Configure, dry-run, or operate the optional AXPA local agent and scheduled task wrapper.

Generate process-owner scorecards from AXPA findings, business owners, high-risk counts, and module context.

Generate role-labeled static dashboard entry points for viewer, DBA, AX admin, QA/GxP, CIO, and admin users.

Evaluate AXPA release gate pass/block decisions from current high and critical findings.

Build a plugin release ZIP with SHA-256 manifest while excluding evidence, output, and cache files.

Analyze SLO burn-rate signals for AXPA playbooks and high-risk finding budgets.

Generate strategic planning outputs including what-if simulation, baseline benchmark, evidence roadmap, remediation Kanban, KPI contracts, and capability matrix.

Generate value realization opportunities and expected proof points from AXPA findings.

Configure, validate, or run the local AXPA RBAC web portal with token-based dashboard access and audit logging.

Map AXPA findings to Trace Parser or DynamicsPerf X++ class/method evidence when normalized trace exports are available.

Forecast likely SLA, capacity, or workload anomalies from AXPA trend evidence.

Propose safer AX batch scheduling based on batch-related findings and SQL pressure.

Translate technical findings into business impact and owner language.

Classify risk for proposed AX/SQL remediation actions before implementation.

Identify AX 2012 performance findings that suggest modernization or D365 migration planning.

Detect missing evidence required for confident AX/SQL diagnosis.

Generate CIO-ready narratives from AXPA evidence.

Explain AXPA findings for technical teams, AX key users, and management.

Draft GxP validation evidence for AX performance changes.

Generate incident summaries with cause, impact, evidence, and next steps.

Convert resolved or recurring findings into reusable knowledge-base learning candidates.

Deduplicate noisy AXPA findings into actionable groups.

Map expensive SQL query evidence to AX tables and candidate X++ owners.

Identify performance regressions after deployments, configuration, index, or schedule changes.