[agentic-token-optimizer] Daily Agentic Workflow AIC Usage Audit — schema pruning, OTEL compression & chart sub-agent #118

Target workflow: Daily Agentic Workflow AIC Usage Audit (agentic-token-audit.md)
Reason selected: Highest-frequency AI workflow not self-targeted; prior June 9 critical recommendation (early exit on empty data) remains unimplemented; fresh June 10–11 run evidence confirms persistent waste pattern.

Analysis period: 2026-06-05 → 2026-06-11 · 5 runs audited

📊 Spend Profile

All 5 audited runs consumed 3.6–7.1 M effective tokens to produce an issue confirming there was nothing to report. The log collection step (gh aw logs --start-date -1d) has consistently returned an empty .runs array, meaning every run executes the full four-phase workflow — Python scripting, snapshot persistence, chart generation attempts, and full template issue creation — before concluding with zero findings.

🏆 Ranked Recommendations

#1 — Enforce early exit after Phase 1 when the log window is empty (still unimplemented from June 9)

Estimated AIC savings: 20–30% per empty-data run

Evidence: 5/5 audited runs detected len(runs) == 0. Despite having nothing to report, every run completes the full workflow: Phase 1 writes and runs a Python script, Phase 2 copies an empty snapshot, Phase 3 attempts chart generation (skipped or generates empty output), and Phase 4 fills the entire report template with zero-valued placeholders. Issue footers confirm 3.6–7.1 M effective tokens consumed per run for this zero-value work. The June 9 optimizer issue first identified this pattern; it has not been acted on.

Action: After Phase 1 completes, add a conditional branch at the top of Phase 2:

If `audit_snapshot.json` shows `overall.total_runs == 0` and the raw `.runs` array was empty,
immediately publish the following minimal issue and exit — skip Phases 2, 3, and 4:

  title: "Daily AIC Usage Audit — {YYYY-MM-DD}"
  body:
    > ⚠️ No agentic workflow runs captured in the last 24 hours.
    > `gh aw logs --start-date -1d` returned an empty `.runs` array.
    > No snapshot, charts, or trend entry recorded.

Eliminating Phases 2–4 on empty-data runs removes the Python snapshot copy, rolling-summary update, chart-generation attempt, and full template expansion — the primary sources of wasted effective tokens.

#2 — Prune the RunData schema table to used fields only

Estimated AIC savings: 10–12% per run

Evidence: The "Data Sources" section contains a 14-row schema table. Only 7 of those fields are referenced in Phase 1 processing or Phase 4 reporting (workflow_name, aic, token_usage, turns, error_count, warning_count, status, conclusion). The remaining fields — effective_tokens (marked "deprecated; use aic for billing"), url, duration, created_at, run_id, token_usage_summary — are described in detail but unused in the processing logic.

Action: Replace the 14-row RunData table with an 8-row table containing only the actively used fields. Remove the deprecated effective_tokens row entirely. The pruned table removes approximately 18 lines of dense context from every run.

| Field | Type | Notes |
|---|---|---|
| `workflow_name` | string | Human-readable name |
| `aic` | float | AI Credits consumed (primary billing metric) |
| `token_usage` | int | Total tokens (treat missing/null as 0) |
| `turns` | int | Number of agent turns |
| `error_count` | int | Errors encountered |
| `warning_count` | int | Warnings encountered |
| `status` | string | `completed`, `in_progress`, etc. |
| `conclusion` | string | `success`, `failure`, etc. |

#3 — Compress the OTEL experiment span section to a 4-line note

Estimated AIC savings: 6–8% per run

Evidence: Lines 276–299 of agentic-token-audit.md contain a 25-line inline JavaScript template for emitting OTEL span attributes. This section fires only when /tmp/gh-aw/experiments/assignments.json exists. No observed run produced an experiment-related event in its step summary, indicating the assignments file was absent in every run. The full JavaScript template adds ~350 tokens of scaffolding to the prompt on every run regardless.

Action: Replace the 25-line OTEL code block with a 4-line prose description:

If `/tmp/gh-aw/experiments/assignments.json` exists, emit one OTEL span attribute per
assignment key using `otlp.cjs` `logSpan`: set `gh_aw.experiment.<name>` to the assigned
variant for each name, and `gh_aw.experiment.names` to a comma-separated list of all names.
Skip this step if the file is absent.

#4 — Remove "Important Notes" section; inline the one unique directive into Phase 2

Estimated AIC savings: 3–4% per run

Evidence: The "Important Notes" section (lines 263–272) contains three directives already present elsewhere:

• Null coalescing → already in Phase 1 ("Handle null/missing aic and token_usage by treating them as 0").
• Empty vs. zero-completed run distinction → already in Phase 2 conditional logic.
• "Keep the issue concise" → editorial guidance with no structural effect.

All three are redundant. The section adds ~11 lines of context with no unique informational value.

Action: Delete the "Important Notes" section entirely. No content is lost — the Phase 1 and Phase 2 instructions already cover both substantive points.

🔧 Structural Optimization: Inline Sub-Agent for Chart Generation

Phase 3 (Generate Charts) qualifies as a strong inline sub-agent candidate. The workflow has no existing ## agent: blocks.

The chart generation task is entirely extractive: read two JSON files, produce two PNG files using matplotlib/seaborn, upload them, return two URLs. A smaller model handles this without the full reasoning capability of the main agent.

Note: Implement alongside or after Recommendation #1 to ensure the sub-agent is not invoked on empty-data runs.

Proposed prompt change — replace the Phase 3 body with:

## agent: charts (model: haiku)
Read `/tmp/gh-aw/token-audit/audit_snapshot.json` (workflow AIC totals) and
`/tmp/gh-aw/repo-memory/default/rolling-summary.json` (historical entries).

1. If fewer than 2 rolling-summary entries exist, skip the trend chart; return `null` for its URL.
2. Generate `ai_credits_by_workflow.png`: horizontal bar chart, top 15 workflows by `total_ai_credits`, 300 DPI, white background.
3. Generate `ai_credits_trend.png`: dual-axis line chart — primary y-axis `total_ai_credits`, secondary y-axis `active_workflows` labeled "Active workflows/day". 300 DPI, white background.
4. `export PYTHONPATH=/tmp/gh-aw/token-audit/site-packages` before any Python command.
5. Upload each PNG via `upload_asset`. Return the two URLs.

⚠️ Caveats

• AIC billing figures are 0.00 across all snapshots; savings estimates are derived from effective-token footprints visible in issue footers (3.6–7.1 M/run) and the June 9 optimizer baseline (0.27 AIC/run ≈ 12% prompt reduction).
• 5 runs analyzed; the empty-data pattern may resolve independently if the gh aw logs data pipeline is fixed upstream.
• The chart sub-agent recommendation delivers savings only when real data is present; all 5 recent runs had no data and chart generation was already being skipped.
• Recommendation Add CI workflow to compile agentic workflows via gh-aw-actions #1 (early exit) was first identified in the June 9 optimizer issue (#113); this issue provides updated evidence confirming the pattern persists across 2 additional runs.

Generated by Agentic Workflow AIC Usage Optimizer · ● 16.4M · ◷

• expires on Jun 18, 2026, 3:52 PM UTC