| name | batch-job-optimization |
| description | Analyze Dynamics AX 2012 batch jobs, SysOperation workloads, collisions, runtime trends, and SQL side effects to recommend safer scheduling and tuning actions. |
Batch Job Optimization
Use this skill when AX batch runtime, nightly processing, retail jobs, AIF/services, or SysOperation workloads are suspected performance drivers.
Inputs
- Batch job and task history with start time, end time, status, company, class, and AOS.
- SQL wait/query/blocking evidence for the same time window.
- AOS event logs, performance counters, and user session patterns.
- Deployment or configuration change timestamps for before/after comparison.
- Historical runtime trends, SLA targets, and maintenance-window boundaries.
- Batch-group and AOS assignment across environments where environment drift is suspected.
Analysis
- Find long-running jobs, retry patterns, failures, and runtime variance.
- Detect overlapping high-load jobs and collisions with user activity, integrations, or maintenance.
- Correlate batch windows with SQL waits, blocking, top queries, TempDB pressure, and file latency.
- Compare normal days with business-critical windows such as month-end close, inventory close, MRP, statement posting, and EDI imports.
- Forecast whether job duration will breach the target window based on recent trends.
- Identify performance debt: recurring jobs whose findings are repeatedly deferred.
- Separate tuning actions into scheduling, parallelism, AX code/query review, SQL maintenance, data lifecycle, and infrastructure capacity.
Collision Patterns
Flag these patterns explicitly:
- Multiple heavy jobs using the same table family in the same window.
- Batch jobs overlapping with index/statistics maintenance, backups, ETL, or reporting.
- Long-running jobs assigned to an overloaded AOS while other AOS nodes are idle.
- Frequent retries or partial failures that increase total load.
- Jobs with rising duration trend after deployments or data-volume growth.
- Posting, settlement, inventory, retail, or integration jobs causing repeat blocking roots.
- Jobs that fit in test but fail in production due to data volume, AOS assignment, or schedule drift.
- Custom batch classes whose SQL signature or Trace Parser stack dominates the runtime.
Recommendations
Prefer low-risk operational changes first: reschedule conflicting jobs, split oversized workloads, adjust batch groups or AOS assignment, and validate maintenance windows. Escalate to SQL or X++ changes only when evidence supports it.
Each recommendation must include expected effect, affected business process, operational risk, validation window, owner, and rollback path. For GxP environments, describe the change as a proposal requiring approval, not an instruction to execute.
SLA Forecast Output
When trend data exists, include:
- Current average runtime and p95 runtime.
- Weekly or monthly growth rate.
- Target window and remaining buffer.
- Predicted breach horizon.
- Confidence and data limitations.
- Recommended decision date.
