>80% faster SAP reporting without manual rework
Autonomous agents optimized runtime performance of critical SAP reports for a large textile manufacturer, resolving timeouts, stabilizing execution, and eliminating operational risk, without altering business logic or report output.
Business-critical reports were failing. The 2027 deadline was looming.
A large textile manufacturer running SAP ECC was preparing for its S/4HANA migration. Over 15+ years, the system had accumulated thousands of custom ABAP programs - many undocumented and written by developers no longer with the organization.
The immediate crisis was operational. Business-critical reports, including the Purchase Register, Order-Delivery-Bill Status, and GST-Report, were timing out after 20+ minutes. The finance team was forced to run reports one day at a time, manually stitching results to keep operations moving.
Traditional remediation estimates exceeded six months of developer effort. With the 2027 ECC sunset approaching and limited ABAP talent available, continuing with a manual approach posed unacceptable risk.
“Traditional remediation estimates came back at 6+ months of developer effort”
Autonomous AI Rewriting of Performance-Critical ABAP
The agents performed deep static analysis, identifying performance anti-patterns throughout the codebase: SELECT * queries inside nested loops, linear table scans where binary search was possible, missing parallel cursor techniques, and legacy syntax incompatible with HANA optimization.
Each program was automatically rewritten to modern ABAP standards. The agents introduced:
Transformation of each program completed in minutes. Automated regression testing confirmed 100% functional parity before delivery.
Data-driven improvements measured on live enterprise workloads.
All legacy reports exceeded acceptable execution limits and failed to complete. Results showed autonomous AI restored business-critical reporting performance without manual remediation or regression risk.
From Legacy to Optimized
LOOP AT t_final ASSIGNING <l_fs_final>.
" Performance Anti-pattern: DB hit in loop
SELECT SINGLE audat FROM vbak INTO (audat)
WHERE vbeln = <l_fs_final>-sono.
ENDLOOP.IF t_final IS NOT INITIAL.
" Bulk Retrieval using FOR ALL ENTRIES
SELECT vbeln, audat FROM vbak
INTO TABLE @DATA(lt_vbak)
FOR ALL ENTRIES IN @t_final
WHERE vbeln = @t_final-sono.
SORT lt_vbak BY vbeln. " Prepare for Binary Search
ENDIF.
LOOP AT t_final ASSIGNING <l_fs_final>.
" O(log n) Read instead of DB call
READ TABLE lt_vbak INTO DATA(ls_vbak)
WITH KEY vbeln = <l_fs_final>-sono
BINARY SEARCH.
IF sy-subrc = 0.
audat = ls_vbak-audat.
ENDIF.
ENDLOOP.See exactly how the agent refactored a linear search into a HANA-optimized binary search pattern.
“Sookti AI replaced seven man-days of ABAP toil with minutes of agent runtime, while simultaneously baking in HANA best-practices we wouldn't have implemented manually.”
Aspect Details
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