Target Workflow
Daily Agentic AI Research Digest (daily-agentic-research.md)
Selected as the highest-AIC non-monitoring workflow not recently optimized with enough gap to re-analyze. All 7 runs in the audit window succeeded; the wide AIC variance (65–151) signals inconsistent browsing behavior that can be tightened with prompt changes alone.
Analysis Period & Runs Analyzed
Period: 2026-08-25 → 2026-08-31 · Runs analyzed: 7
Spend Profile
| Metric |
Value |
| Total AIC (7 runs) |
724.83 |
| Avg AIC / run |
103.55 |
| Min / Max AIC |
65.13 / 150.50 |
| AIC variance (max/min) |
2.3× |
| Avg action minutes |
~6 min |
| Turns (latest run) |
14 |
| Tokens (latest run) |
433 553 |
| GitHub API calls / run |
10 (consistent) |
| Error / warning count |
0 / 0 |
| Cache efficiency |
No data (web-fetch only) |
Per-run AIC breakdown
Ranked Recommendations
1 · Cap source browsing at 2 fetches — est. −15–25 AIC/run
Evidence: AIC swings 2.3× between runs (65→150). The prompt says "Browse 2–3 of these sources (don't fetch all if you find a strong candidate early)" — but the vague upper-bound allows the agent to fetch 3 or all 4 listed URLs on borderline runs. High-AIC runs (138–150) likely involve 3–4 web fetches plus follow-up reads. Lowering the hard cap to 2 removes this ambiguity.
Action: In ## Browsing Instructions, change:
Browse 2–3 of these sources (don't fetch all if you find a strong candidate early).
to:
Fetch at most 2 sources. Stop as soon as you find one qualifying candidate — do not continue browsing.
2 · Collapse the 5-area Research Strategy into a single directive — est. −8–15 AIC/run
Evidence: The ## Research Strategy section enumerates 5 distinct research areas and instructs the agent to survey them before selecting a source. With 14 turns in the latest run, significant turns are being spent mentally mapping areas to sources before a single fetch occurs. A flatter instruction removes this planning overhead.
Action: Replace the 5-bullet list in ## Research Strategy with:
Find the single most recent, impactful finding in agentic AI — any area counts. Prioritize novelty and practical relevance to workflow developers.
The detailed area list can be retained as a collapsed reference or removed entirely; the ## Selection Criteria section already captures the quality bar.
3 · Remove low-signal URLs from Browsing Instructions — est. −5–10 AIC/run
Evidence: The prompt lists 4 fetch targets. Two (OpenAI news, Anthropic news) are corporate announcement feeds that infrequently publish research-grade content relevant to agentic optimization. Fetching them uses a full web-fetch turn even when they yield nothing useful. HuggingFace Papers and arXiv together cover the research signal more reliably.
Action: Remove (openai.com/redacted) and (www.anthropic.com/redacted) from the default source list in ## Browsing Instructions. Keep them as optional fallback sources if the agent finds nothing on HuggingFace/arXiv, or remove them entirely.
Tool Usage
| Tool |
Configured |
Observed |
Verdict |
web-fetch |
✅ |
✅ every run |
Keep |
No unused tools; no tool removal recommended.
Structural Optimization
Inline sub-agents: Not recommended. This workflow has a single, tightly-coupled pipeline: fetch → evaluate → write discussion. Sections cannot run independently (source selection depends on browse results; output depends on selection). No candidate scores ≥ 4.
Setup prefix: No repeated setup calls across sections. Not applicable.
Caveats
- Token and turn data are only available for 1 of 7 runs; AIC variance is the primary signal.
- Recommendations 1 and 2 are complementary — both address the same root cause (unconstrained browsing scope). Applying both is expected to stack savings.
- Estimated savings assume the high-AIC runs (138–150) are the result of 3–4 fetches; this is inferred from AIC magnitude, not a directly observed tool trace.
References: §33379155659 · §33245970786 · §33175306132
Generated by Agentic Workflow AIC Usage Optimizer · 190.3 AIC · ⊞ 21.6K · ◷
Target Workflow
Daily Agentic AI Research Digest (
daily-agentic-research.md)Selected as the highest-AIC non-monitoring workflow not recently optimized with enough gap to re-analyze. All 7 runs in the audit window succeeded; the wide AIC variance (65–151) signals inconsistent browsing behavior that can be tightened with prompt changes alone.
Analysis Period & Runs Analyzed
Period: 2026-08-25 → 2026-08-31 · Runs analyzed: 7
Spend Profile
Per-run AIC breakdown
Ranked Recommendations
1 · Cap source browsing at 2 fetches — est. −15–25 AIC/run
Evidence: AIC swings 2.3× between runs (65→150). The prompt says "Browse 2–3 of these sources (don't fetch all if you find a strong candidate early)" — but the vague upper-bound allows the agent to fetch 3 or all 4 listed URLs on borderline runs. High-AIC runs (138–150) likely involve 3–4 web fetches plus follow-up reads. Lowering the hard cap to 2 removes this ambiguity.
Action: In
## Browsing Instructions, change:to:
2 · Collapse the 5-area Research Strategy into a single directive — est. −8–15 AIC/run
Evidence: The
## Research Strategysection enumerates 5 distinct research areas and instructs the agent to survey them before selecting a source. With 14 turns in the latest run, significant turns are being spent mentally mapping areas to sources before a single fetch occurs. A flatter instruction removes this planning overhead.Action: Replace the 5-bullet list in
## Research Strategywith:The detailed area list can be retained as a collapsed reference or removed entirely; the
## Selection Criteriasection already captures the quality bar.3 · Remove low-signal URLs from Browsing Instructions — est. −5–10 AIC/run
Evidence: The prompt lists 4 fetch targets. Two (OpenAI news, Anthropic news) are corporate announcement feeds that infrequently publish research-grade content relevant to agentic optimization. Fetching them uses a full
web-fetchturn even when they yield nothing useful. HuggingFace Papers and arXiv together cover the research signal more reliably.Action: Remove
(openai.com/redacted) and(www.anthropic.com/redacted) from the default source list in## Browsing Instructions. Keep them as optional fallback sources if the agent finds nothing on HuggingFace/arXiv, or remove them entirely.Tool Usage
web-fetchNo unused tools; no tool removal recommended.
Structural Optimization
Inline sub-agents: Not recommended. This workflow has a single, tightly-coupled pipeline: fetch → evaluate → write discussion. Sections cannot run independently (source selection depends on browse results; output depends on selection). No candidate scores ≥ 4.
Setup prefix: No repeated setup calls across sections. Not applicable.
Caveats
References: §33379155659 · §33245970786 · §33175306132