Defined runtime boundaries and evidence logging architecture

2 min read

Defined runtime boundaries and evidence logging architecture

  • Day: 2026-05-19
  • Time: 11:10 to 11:20
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: HIGH
  • Assignee: Matías Nehuen Iglesias
  • Tags: Architecture, Observability, Logging, Git, Automation, Evidence

Description

Session Goal

Clarify the execution model for the office/staff/ops runtime by separating module responsibilities, defining safe command surfaces, and establishing evidence/logging practices before adding more automation.

Key Activities

  • Proposed a cleaner runtime architecture that splits attention compilation, packet preparation, bounded project checks, and time-window evidence reconstruction into distinct responsibilities.
  • Defined the need for explicit CLI run modes, standalone evidence collectors, and a thin command router to avoid a single god entrypoint coupling unrelated workflows.
  • Shifted the design discussion from component layout to execution boundaries: which modules may trigger work, which only emit artifacts, and where scheduling should live.
  • Drafted Python utilities for git and filesystem tracing to collect JSONL evidence across repositories, including repo discovery, commit metadata extraction, file-change counting, and CLI entrypoints for trace generation.
  • Established an operational logging model with three tiers: compact daily ledgers, per-run logs, and on-demand debug traces.
  • Emphasized that observability should come before scheduled jobs, so timers and automation remain explainable rather than opaque.
  • Added a cautious Git push checklist for inspecting untracked files, validating staged changes, checking for credentials, committing safely, and pushing to origin/main.

Achievements

  • The runtime design now has a clearer separation between orchestration, artifact generation, and evidence reconstruction.
  • Logging and observability requirements were specified early, reducing the risk of introducing hidden or hard-to-debug scheduled automation.
  • Evidence collection tooling and safe Git workflow guidance were concretely outlined, giving the system a traceable operational path.

Pending Tasks

  • Implement the module boundary decisions in code and wire them into the runtime.
  • Build or integrate the evidence collectors and logging layers into the actual workflow.
  • Decide final scheduler ownership and attach schedules only to the modules that are allowed to trigger work.
  • Validate the Git workflow against the current repository state before pushing changes.

Evidence

  • source_file=2026-05-19.sessions.jsonl, line_number=9, event_count=0, session_id=21a5d855fe65d79ddf121db583b9d113340cba5ee43252852166987778ae09da
  • event_ids: []

Graph View

Backlinks