Target workflow: Agentic Workflow AIC Usage Optimizer (agentic-token-optimizer.md)
Reason selected: Highest-AIC workflow in the 7-day window (2,782 total AIC / 5 runs). All 5 tracked workflows fall within the 14-day cooldown; this one was last optimized 7 days ago (2026-06-18), making it the best available candidate. Running in the source repository (githubnext/agentic-ops) so the AIC-monitoring-family exclusion does not apply.
Spend Profile
| Metric |
All 5 runs |
Excl. June 18 outlier (4 runs) |
| Total AIC |
2,782 |
1,748 |
| Avg AIC / run |
556 |
437 |
| Total tokens |
~6.85M |
~5.25M |
| Avg tokens / run |
~1.71M |
~1.31M |
| Avg turns / run |
~36 |
~28 |
| Cache efficiency |
N/A |
N/A |
| Success rate |
100% (5/5) |
100% |
June 18 run (1,034 AIC, 58 turns, 3.06M tokens) is treated as a post-change baseline outlier. The four subsequent runs form the stable baseline at ~437 AIC/run, ~28 turns/run.
Per-run breakdown
Token data sourced from daily audit snapshots; June 19 snapshot recorded the prior day (June 18) run.
Spend driver: Turn count is the primary AIC lever (24–35 turns/run). Each turn re-sends the full system prompt (~203 lines of prompt body ≈ 7–8k tokens). Reducing prompt verbosity saves tokens proportionally across every turn.
Ranked Recommendations
1. Trim the ## Data Access Guidelines section (estimated −15–20 AIC/run)
What: The current 27-line section contains three fully-annotated bash code blocks with ✅/❌ markers illustrating gh api --jq filtering and chained commands. These patterns are fundamental best-practices that the agent already applies correctly in every audited run.
Action: Replace the three code blocks and their prose commentary with a single 2-sentence directive:
Always filter gh api responses with --jq or pipe through jq — never load unfiltered payloads. Combine multi-step reads into one bash block using pipes and &&.
Remove the ✅/❌ annotated examples entirely. Retain the opening paragraph if it helps orient the agent. This reduces the section from 27 lines to ~4 lines, saving ~600–700 tokens per turn.
Evidence: All 5 runs completed successfully; the agent consistently used --jq filtering and batched commands. The instructional examples add no observable runtime benefit.
Estimated saving: ~600 tokens × 28 avg turns = ~17k tokens/run ≈ 15–20 AIC/run.
2. Compress the Phase 4 inline sub-agent scoring rubric (estimated −10–15 AIC/run)
What: The ### Inline Sub-Agent Opportunity Analysis subsection spans ~32 lines: a 4-row scoring table, three scoring threshold bullets, a 6-item "smaller models are a good fit for" list, and a "keep with main agent when" paragraph. The qualitative guidance duplicates what the 4-column table already encodes.
Action: Remove the 6-item "smaller models are a good fit for" list and the "keep with main agent when" paragraph (~12 lines). Replace them with one sentence:
Small models suit extractive, classificatory, or single-file-summarisation work; keep cross-source synthesis and the final issue body in the main agent.
Retain the scoring table and the 6+ / 4–5 / <4 thresholds. This reduces Phase 4 from 47 lines to ~33 lines, saving ~450–550 tokens per turn.
Evidence: In the last 3 runs the optimizer identified 0 sub-agent candidates each time. The expanded qualitative guidance is processed every run but rarely acts as the deciding factor.
Estimated saving: ~500 tokens × 28 avg turns = ~14k tokens/run ≈ 10–15 AIC/run.
3. Remove the frontmatter-only code example from Phase 3 (estimated −5–10 AIC/run)
What: ## Phase 3 — Read Workflow Source contains four code blocks (28 lines): full-source read, frontmatter-only read, prompt-body-only read, plus a "Validate from the source" checklist. The frontmatter-only block (awk '/^---$/{n++; if(n==2) exit} n==1') is almost never needed — the agent reads either the full file or the prompt body.
Action: Remove the frontmatter-only code block and its surrounding commentary (~7 lines). Retain the full-source and prompt-body examples. This saves ~250–300 tokens per turn.
Evidence: No recent run needed to extract frontmatter independently; the validation checklist (configured tools, prompt structure, inline sub-agents) is satisfied by reading the prompt body section.
Estimated saving: ~275 tokens × 28 avg turns = ~8k tokens/run ≈ 5–10 AIC/run.
Combined Estimated Impact
| # |
Recommendation |
Est. savings/run |
| 1 |
Trim Data Access Guidelines |
15–20 AIC |
| 2 |
Compress Phase 4 scoring rubric |
10–15 AIC |
| 3 |
Remove Phase 3 frontmatter-only example |
5–10 AIC |
|
Total |
30–45 AIC/run (~7–10% of 437 AIC baseline) |
Secondary effect: shorter, more focused instructions may reduce avg turns from ~28 toward ~24 (where the June 23 run landed), yielding an additional 10–15% AIC reduction beyond the direct token savings.
Caveats
- Sampling: 5 runs analyzed; 1 (June 18) treated as an outlier. Estimates assume the stable 4-run baseline (437 AIC/run). Token data unavailable for the June 19 run.
- 14-day cooldown: All 5 active workflows were optimized within the last 14 days. This recommendation targets the oldest-cooldown workflow; implement after confirming prior optimizations have stabilised.
- Turn-count variance: Run turns ranged 24–35 (+46% spread). AIC savings scale proportionally — higher-turn runs will see more benefit from prompt trimming.
- No reliability issues: All 5 runs succeeded with 0 errors and 0 warnings; no reliability-based recommendations are warranted.
- Sub-agents not recommended: The optimizer is a sequential analytical pipeline where each phase depends on prior phase outputs. No phase scored ≥6 on the independence/parallelism criteria.
References: §27966098529 · §28036893325 · §28109263044
Generated by Agentic Workflow AIC Usage Optimizer · 271.9 AIC · ⊞ 21.6K · ◷
Target workflow: Agentic Workflow AIC Usage Optimizer (
agentic-token-optimizer.md)Reason selected: Highest-AIC workflow in the 7-day window (2,782 total AIC / 5 runs). All 5 tracked workflows fall within the 14-day cooldown; this one was last optimized 7 days ago (2026-06-18), making it the best available candidate. Running in the source repository (
githubnext/agentic-ops) so the AIC-monitoring-family exclusion does not apply.Spend Profile
Per-run breakdown
Token data sourced from daily audit snapshots; June 19 snapshot recorded the prior day (June 18) run.
Spend driver: Turn count is the primary AIC lever (24–35 turns/run). Each turn re-sends the full system prompt (~203 lines of prompt body ≈ 7–8k tokens). Reducing prompt verbosity saves tokens proportionally across every turn.
Ranked Recommendations
1. Trim the
## Data Access Guidelinessection (estimated −15–20 AIC/run)What: The current 27-line section contains three fully-annotated bash code blocks with ✅/❌ markers illustrating
gh api --jqfiltering and chained commands. These patterns are fundamental best-practices that the agent already applies correctly in every audited run.Action: Replace the three code blocks and their prose commentary with a single 2-sentence directive:
Remove the ✅/❌ annotated examples entirely. Retain the opening paragraph if it helps orient the agent. This reduces the section from 27 lines to ~4 lines, saving ~600–700 tokens per turn.
Evidence: All 5 runs completed successfully; the agent consistently used
--jqfiltering and batched commands. The instructional examples add no observable runtime benefit.Estimated saving: ~600 tokens × 28 avg turns = ~17k tokens/run ≈ 15–20 AIC/run.
2. Compress the Phase 4 inline sub-agent scoring rubric (estimated −10–15 AIC/run)
What: The
### Inline Sub-Agent Opportunity Analysissubsection spans ~32 lines: a 4-row scoring table, three scoring threshold bullets, a 6-item "smaller models are a good fit for" list, and a "keep with main agent when" paragraph. The qualitative guidance duplicates what the 4-column table already encodes.Action: Remove the 6-item "smaller models are a good fit for" list and the "keep with main agent when" paragraph (~12 lines). Replace them with one sentence:
Retain the scoring table and the
6+ / 4–5 / <4thresholds. This reduces Phase 4 from 47 lines to ~33 lines, saving ~450–550 tokens per turn.Evidence: In the last 3 runs the optimizer identified 0 sub-agent candidates each time. The expanded qualitative guidance is processed every run but rarely acts as the deciding factor.
Estimated saving: ~500 tokens × 28 avg turns = ~14k tokens/run ≈ 10–15 AIC/run.
3. Remove the frontmatter-only code example from Phase 3 (estimated −5–10 AIC/run)
What:
## Phase 3 — Read Workflow Sourcecontains four code blocks (28 lines): full-source read, frontmatter-only read, prompt-body-only read, plus a "Validate from the source" checklist. The frontmatter-only block (awk '/^---$/{n++; if(n==2) exit} n==1') is almost never needed — the agent reads either the full file or the prompt body.Action: Remove the frontmatter-only code block and its surrounding commentary (~7 lines). Retain the full-source and prompt-body examples. This saves ~250–300 tokens per turn.
Evidence: No recent run needed to extract frontmatter independently; the validation checklist (configured tools, prompt structure, inline sub-agents) is satisfied by reading the prompt body section.
Estimated saving: ~275 tokens × 28 avg turns = ~8k tokens/run ≈ 5–10 AIC/run.
Combined Estimated Impact
Secondary effect: shorter, more focused instructions may reduce avg turns from ~28 toward ~24 (where the June 23 run landed), yielding an additional 10–15% AIC reduction beyond the direct token savings.
Caveats
References: §27966098529 · §28036893325 · §28109263044