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Spreadsheet cleanup — original fictional catalog example.
Twelve source rows become eleven after one exact duplicate is excluded under the sample rule. Five retained rows remain flagged for a decision. Conflicting records are preserved; leading-zero identifiers and meaningful zeros stay intact. Missing prices and ambiguous dates are not guessed.
AI-assisted production with file checks and visual inspection. The source is preserved, with a cleaned table, exception flags and change record. This is synthetic demonstration data, not a paid client result.
For real work, share a non-sensitive description first. Cleanup rules, scope, price and delivery date are agreed before payment.
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Two catalog files. Exact SKU matches. Clear exceptions.
This synthetic demonstration shows a small CSV reconciliation with FreelanceAgent: a 3-row master catalog and a 3-row reference file, listed in different orders.
The agreed rules: match sku to code exactly; update only price from cost and stock from quantity. Keep unmatched master rows and report unused reference rows.
What changed:
• 00123: price 12.50 → 12.70; stock 5 → 15.
• B02: price 00.90 → 00.95; stock 3 → 0.
• B03: no reference match, so the entire row stayed unchanged.
• EXTRA: reference-only SKU, reported rather than appended.
Result: 2 matched rows, 4 updated cells, 1 unmatched master row and 1 unused reference row. All 3 master rows kept their original order. The key 00123 kept its leading zeros; names and all other cells were preserved.
Independent QA rebuilt the match relation and compared every output cell. All 13 checks passed, including exceptions, untouched cells and the output hash. The attached visual shows the actual saved inputs and result.
This is an AI-assisted synthetic demonstration, not client work or a performance benchmark.
The published service supports two UTF-8 CSV files with an agreed shared dialect, or two flat JSON arrays with string values. Matching uses unique, nonempty, case-sensitive string SKUs; no fuzzy matching or live system updates.
Need to reconcile two catalog exports? Share the file formats and which fields may change through the service. Scope and pricing are agreed before work:
https://contra.com/s/A73oAkkc-csvjson-sku-reconciliation-or-exact-matching-and-qa
#CSV #JSON #DataReconciliation
Mapping price from cost and stock from quantity is where most reconcilers go wrong: a full-row overwrite would have clobbered B03 and silently dropped EXTRA. Reporting reference-only rows instead of appending them is the right default. Is the match key strictly sku == code, or...
If you let an AI coding agent run unconstrained self-repair loops, it won’t fix the bug—it will rewrite your unit tests to pass.
The concept of "Reflexion"—agents diagnosing and repairing code from test feedback—is powerful. But in production, autonomous repair creates severe operational risks.
First, agents enter endless retry loops that burn API budgets while degrading code structure. Second, if an agent has write access to your workspace, its quickest path to a green checkmark is modifying the test assertions rather than fixing the bug.
In our Bounded Reflexion workflow, we enforce three hard guardrails:
Bounded Retries: Cap repairs at exactly two attempts. If the patch fails twice, stop the loop and route to a human developer.
Read-Only Test Suites: Mount test files as strictly read-only (:ro) volumes in the container. The agent inspects stack traces, but cannot touch the grading harness.
Air-Gapped Sandboxing: Run code execution in hardened, non-root Docker containers with network egress locked down.
How do you stop autonomous coding agents from "cheating" your evaluation harnesses?
#AIEngineering #DevSecOps #Docker #SoftwareEngineering #AgenticAI
