Target Workflow
Daily Agentic AI Research Digest (daily-agentic-research.md)
Selected as the highest-AIC non-monitoring workflow with no optimization in the current analysis window (last optimized 2026-07-29, 7 days before this audit). All other candidate workflows were optimized within the past 6 days.
Analysis Period
2026-07-30 → 2026-08-05 · 7 runs analyzed
Spend Profile
| Metric |
Value |
| Run count |
7 |
| Total AIC |
615.07 |
| Avg AIC / run |
87.87 |
| Total tokens |
161,319 |
| Avg tokens / run |
23,045 |
| Avg turns / run |
11.9 |
| Success rate |
7 / 7 (100%) |
| Avg duration |
7.1 min |
| Cache efficiency |
Low (web content changes daily) |
Key observation — bimodal AIC distribution:
| Cluster |
Runs |
AIC range |
Duration range |
| Cheap (1–2 sources fetched) |
5 |
54–76 AIC |
4.5–7.0 min |
| Expensive (3–4 sources fetched) |
2 |
152–153 AIC |
6.6–16.4 min |
The 2 expensive runs cost ~2.5× a typical run. If they had been capped at the cheap-run ceiling, total spend would have dropped from 615 to ~435 AIC — a 29% reduction.
Ranked Recommendations
1. Enforce a hard cap of 2 source fetches per run
Estimated savings: ~26 AIC/run (avg) · High confidence
The current instruction is advisory: "Browse 2–3 of these sources (don't fetch all if you find a strong candidate early)." In 2 of 7 runs the model fetched all 4 listed sources, inflating AIC to 152 AIC vs the ~62 AIC baseline.
Action: Replace the soft guidance with an explicit hard limit:
Before: "Browse 2–3 of these sources (don't fetch all if you find a strong candidate early)."
After: "Fetch at most 2 sources. Stop immediately once you identify a viable candidate — do not fetch additional sources."
This single change eliminates the expensive-run cluster without affecting output quality; all successful runs produce the same one-finding discussion regardless of source count.
Evidence: 5/7 runs completed under 76 AIC using ≤2 sources. The 2 expensive runs match expected cost when fetching all 4 sources (4× fetches ≈ 2.5× AIC, consistent with model traversal overhead).
2. Reduce source list from 4 to 2 primary sources
Estimated savings: ~8–12 AIC/run · Medium confidence
The current list includes 4 sources: huggingface.co/papers, arxiv.org, openai.com/news, anthropic.com/news. The company news pages (OpenAI, Anthropic) publish infrequently and rarely contain the technically specific, peer-reviewed findings that score highest on the selection criteria. Including them invites unnecessary fetches when the primary academic sources turn up nothing immediately.
Action: Remove openai.com/news and anthropic.com/news from the primary list. If desired, retain them as explicit fallback: "If neither primary source yields a candidate, try openai.com/news or anthropic.com/news."
This pairs well with Recommendation 1 and makes the 2-fetch cap a complete constraint rather than a forced tradeoff.
3. Trim Research Strategy from 5 topic areas to 3
Estimated savings: ~2–4 AIC/run · Low-to-medium confidence
The ## Research Strategy section lists 5 topic areas. Providing more topic axes than the model can meaningfully resolve per session increases context length and may encourage broader browsing. The first 3 topics (multi-agent orchestration, LLM inference efficiency, tool-use optimization) are the most directly actionable for the target audience.
Action: Remove the last 2 topic bullets (Context management and Agent reliability) from ## Research Strategy, or condense them into a single note: "Also consider context management and reliability findings if especially novel."
Estimated prompt token reduction: ~80 tokens; minor AIC savings individually but compounding across 7+ runs/week.
Caveats
- Run sample is 7 days (7 runs). The bimodal pattern is consistent but a larger window would confirm whether expensive runs correlate with specific days or source availability.
- All 7 runs produced successful discussions; no reliability issues observed.
- Cache efficiency is structurally limited by daily-changing web content; no caching recommendations apply.
- No sub-agent refactor recommended: the workflow is a single sequential flow (fetch → select → write) with no independently parallelizable sections. Splitting would add overhead without reducing AIC.
Run-level evidence
References: §30534505709 · §30743250381 · §30695259200
Generated by Agentic Workflow AIC Usage Optimizer · 118.1 AIC · ⊞ 21.6K · ◷
Target Workflow
Daily Agentic AI Research Digest (
daily-agentic-research.md)Selected as the highest-AIC non-monitoring workflow with no optimization in the current analysis window (last optimized 2026-07-29, 7 days before this audit). All other candidate workflows were optimized within the past 6 days.
Analysis Period
2026-07-30 → 2026-08-05 · 7 runs analyzed
Spend Profile
Key observation — bimodal AIC distribution:
The 2 expensive runs cost ~2.5× a typical run. If they had been capped at the cheap-run ceiling, total spend would have dropped from 615 to ~435 AIC — a 29% reduction.
Ranked Recommendations
1. Enforce a hard cap of 2 source fetches per run
Estimated savings: ~26 AIC/run (avg) · High confidence
The current instruction is advisory: "Browse 2–3 of these sources (don't fetch all if you find a strong candidate early)." In 2 of 7 runs the model fetched all 4 listed sources, inflating AIC to 152 AIC vs the ~62 AIC baseline.
Action: Replace the soft guidance with an explicit hard limit:
This single change eliminates the expensive-run cluster without affecting output quality; all successful runs produce the same one-finding discussion regardless of source count.
Evidence: 5/7 runs completed under 76 AIC using ≤2 sources. The 2 expensive runs match expected cost when fetching all 4 sources (4× fetches ≈ 2.5× AIC, consistent with model traversal overhead).
2. Reduce source list from 4 to 2 primary sources
Estimated savings: ~8–12 AIC/run · Medium confidence
The current list includes 4 sources:
huggingface.co/papers,arxiv.org,openai.com/news,anthropic.com/news. The company news pages (OpenAI, Anthropic) publish infrequently and rarely contain the technically specific, peer-reviewed findings that score highest on the selection criteria. Including them invites unnecessary fetches when the primary academic sources turn up nothing immediately.Action: Remove
openai.com/newsandanthropic.com/newsfrom the primary list. If desired, retain them as explicit fallback: "If neither primary source yields a candidate, try openai.com/news or anthropic.com/news."This pairs well with Recommendation 1 and makes the 2-fetch cap a complete constraint rather than a forced tradeoff.
3. Trim Research Strategy from 5 topic areas to 3
Estimated savings: ~2–4 AIC/run · Low-to-medium confidence
The
## Research Strategysection lists 5 topic areas. Providing more topic axes than the model can meaningfully resolve per session increases context length and may encourage broader browsing. The first 3 topics (multi-agent orchestration, LLM inference efficiency, tool-use optimization) are the most directly actionable for the target audience.Action: Remove the last 2 topic bullets (Context management and Agent reliability) from
## Research Strategy, or condense them into a single note: "Also consider context management and reliability findings if especially novel."Estimated prompt token reduction: ~80 tokens; minor AIC savings individually but compounding across 7+ runs/week.
Caveats
Run-level evidence
References: §30534505709 · §30743250381 · §30695259200