Planned modular fixes for financial evidence and reporting

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Planned modular fixes for financial evidence and reporting

  • Day: 2026-04-18
  • Time: 10:30 to 10:35
  • Project: Accounting
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: HIGH
  • Assignee: Matías Nehuen Iglesias
  • Tags: Financial-Evidence, Debt-Layer, Metrics-Debugging, Reporting-Architecture, Qa

Description

Session Goal

Consolidate a set of planning and diagnostic notes around MAL financial evidence layers, debt materialization, P&L validation, and the front-end reporting architecture. The session aimed to define a practical implementation path that improves accounting observability without destabilizing the existing pipeline.

Key Activities

  • Defined a five-layer financial case structure separating executive framing, numeric evidence, patrimonial prudence, observability, and action demands.
  • Proposed six implementation batches to address missing debt materialization, annual/quarterly income statement reshaping, labels, cost taxonomy, and QA checks.
  • Recommended a cutoff-based debt snapshot layer as a more robust approach than rebuilding a fragile full chronological timeline.
  • Diagnosed the likely cause of missing debt metrics as a MetricsContext loading / path-resolution failure, rather than a registry or builder issue.
  • Analyzed a P&L inconsistency where IS.RENT.TOTAL appears to materialize but triggers checker warnings, while IS.INCOME.TOTAL fails in quarterly/yearly derivations.
  • Proposed a modular front-report architecture: an orchestrator plus specialized block factories, with reusable narrative blocks and separate outputs by audience.
  • Suggested a pragmatic implementation strategy centered on a single front factory megafile (human_balance_front_factory.py) that composes existing table libraries without introducing new accounting logic.
  • Added implementation guidance to keep the front layer conservative, preserve backward compatibility, and avoid architectural drift.

Achievements

  • Clarified the preferred accounting strategy for debt: as-of-date snapshotting with explicit sign conventions and counterparty normalization.
  • Identified the most likely failure mode for missing debt metrics and the next debugging focus: artifact loading into MetricsContext.
  • Narrowed the P&L issue to a likely downstream derivation break and a checker mismatch, with minimal-change remediation as the preferred path.
  • Established a coherent migration direction for reporting: modular composition over monolithic rendering, while preserving the legacy table library.
  • Produced a concrete roadmap for Codex-assisted implementation, including stub-first scaffolding and phased completion.

Pending Tasks

  • Implement and test the debt snapshot layer with validation guards and reproducible regression checks.
  • Fix MetricsContext loading/path resolution so debt artifacts are actually materialized into metric values.
  • Debug the IS.INCOME.TOTAL quarterly/yearly derivation chain and align the checker with the intended source of truth.
  • Complete the human_balance_front_factory.py stub and verify it does not break the current CLI/pipeline compatibility.
  • Add QA checks for labels, cost taxonomy, temporal reshaping, and narrative consistency across the reporting layers.

Evidence

  • source_file=2026-04-18.sessions.jsonl, line_number=4, event_count=0, session_id=e4907e172d0d9b9cd872a2889865a6a00791da7ac6b14505933b9f68b46825e5
  • event_ids: []

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