Target Workflow
Daily Agentic AI Research Digest (daily-agentic-research.md)
Selected because it has the highest AIC spend (417.9 AI credits over 7 days) among non-monitoring workflows whose last optimization window has most elapsed. All 7 runs succeeded, making this a good efficiency target with no reliability issues to address first.
Analysis Period
2026-07-17 → 2026-07-23 · 7 runs analyzed · All conclusions: success
Spend Profile
| Metric |
Value |
| Total AIC (7 days) |
417.93 |
| Avg AIC / run |
59.70 |
| AIC range |
37.0 – 89.2 (2.4× variance) |
| Total tokens (1 run w/ data) |
191,267 |
| Avg turns (1 run w/ data) |
7 |
| Avg action minutes |
5.3 min |
| Cache efficiency |
Unknown (token breakdown not available) |
The 2.4× AIC variance (37 → 89 credits) is the headline signal: some runs cost more than twice others for identical output. This points to inconsistent browsing depth rather than content complexity.
Ranked Recommendations
1. Harden the early-exit directive (est. −12–18 AIC/run)
Current instruction:
Browse 2–3 of these sources (don't fetch all if you find a strong candidate early).
This is advisory, not imperative. On high-AIC runs, the agent likely fetches 3+ sources even after finding a strong candidate, because the soft language ("don't fetch all if...") leaves room for "just one more check."
Proposed change — replace that sentence with:
Fetch sources in the order listed. Stop as soon as you have one strong candidate — do not fetch the next source. Never fetch more than 2 sources per run.
Expected effect: caps fetches at 2 (vs current 2–3) and eliminates the high-end tail. Brings the high-outlier runs (89 AIC on 2026-07-20) closer to the median.
Evidence: §29735272885 (89.2 AIC) vs §29572718682 (37.0 AIC) — same workflow, same output shape, 2.4× cost difference.
2. Trim the network allowlist to match actual sources (est. −0–3 AIC/run, reliability improvement)
The frontmatter allows 10 external domains but the Browsing Instructions only list 4 sources (huggingface, arxiv, openai, anthropic). Six allowed domains have no corresponding fetch instruction:
- "mistral.ai"
- "research.google"
- "deepmind.google"
- "ai.meta.com"
- "blog.langchain.dev"
- "www.pinecone.io"
These domains are reachable but never fetched in observed runs. Keeping them in the allowlist slightly expands the agent's surface for speculative fetches (e.g., if it finds a Mistral blog link inside an OpenAI page and follows it).
Proposed change: Remove the 6 unlisted domains from the network.allowed block, or add explicit # not a primary source comments so future maintainers know they're intentionally off the browse path.
3. Add explicit source priority ordering (est. −5–10 AIC/run)
The four sources in Browsing Instructions are listed without priority. On days when all sources are sparse, the agent may fetch all four to triangulate. Adding an explicit fallback chain reduces this:
Proposed addition at top of Browsing Instructions:
Start with huggingface.co/papers (most consistently fresh). If no strong candidate found there, try arxiv.org. Only proceed to OpenAI/Anthropic news if still empty-handed.
This turns the "explore all" pattern into a "first-match" pattern, consistent with Recommendation 1.
Run-level evidence table
Tool Usage Audit
| Tool |
Configured |
Observed |
Recommendation |
web-fetch |
✅ |
✅ (all runs) |
Keep |
Only one tool configured; it is used in every run. No tool removals warranted.
Inline Sub-Agent Analysis
The workflow has 4 major sections (Research Strategy, Browsing Instructions, Selection Criteria, Output). Scoring:
| Section |
Independence |
Small-model fit |
Parallelism |
Size |
Total |
| Browsing + fetch |
1 |
1 |
0 |
1 |
3 |
| Selection + write |
0 |
1 |
0 |
1 |
2 |
No section scores ≥ 4. The fetch and selection steps are tightly coupled (selection requires seeing fetched content), and the final write is synthesis. No inline sub-agents recommended.
Summary of Estimated Savings
| Recommendation |
Est. AIC saved / run |
| Harden early-exit directive |
12–18 |
| Add explicit source priority |
5–10 |
| Trim network allowlist |
0–3 |
| Total |
17–31 |
At avg 59.7 AIC/run, these changes could reduce spend by 28–52% on the high-variance runs, bringing the expected average closer to 40–45 AIC/run.
Caveats
- Token-level data (cache hit rate, input/output split) is only available for 1 of 7 runs; turn count and prompt size estimates carry uncertainty.
- AIC variance may partially reflect genuine content variation (dense paper days vs. slow news days), not purely over-fetching. The early-exit change should be monitored for a week after implementation.
- This workflow was last optimized 2026-07-20; some prior recommendations may not yet be reflected in the current source.
Generated by Agentic Workflow AIC Usage Optimizer · 113.9 AIC · ⊞ 21.6K · ◷
Target Workflow
Daily Agentic AI Research Digest (
daily-agentic-research.md)Selected because it has the highest AIC spend (417.9 AI credits over 7 days) among non-monitoring workflows whose last optimization window has most elapsed. All 7 runs succeeded, making this a good efficiency target with no reliability issues to address first.
Analysis Period
2026-07-17 → 2026-07-23 · 7 runs analyzed · All conclusions:
successSpend Profile
The 2.4× AIC variance (37 → 89 credits) is the headline signal: some runs cost more than twice others for identical output. This points to inconsistent browsing depth rather than content complexity.
Ranked Recommendations
1. Harden the early-exit directive (est. −12–18 AIC/run)
Current instruction:
This is advisory, not imperative. On high-AIC runs, the agent likely fetches 3+ sources even after finding a strong candidate, because the soft language ("don't fetch all if...") leaves room for "just one more check."
Proposed change — replace that sentence with:
Expected effect: caps fetches at 2 (vs current 2–3) and eliminates the high-end tail. Brings the high-outlier runs (89 AIC on 2026-07-20) closer to the median.
Evidence: §29735272885 (89.2 AIC) vs §29572718682 (37.0 AIC) — same workflow, same output shape, 2.4× cost difference.
2. Trim the network allowlist to match actual sources (est. −0–3 AIC/run, reliability improvement)
The frontmatter allows 10 external domains but the Browsing Instructions only list 4 sources (huggingface, arxiv, openai, anthropic). Six allowed domains have no corresponding fetch instruction:
These domains are reachable but never fetched in observed runs. Keeping them in the allowlist slightly expands the agent's surface for speculative fetches (e.g., if it finds a Mistral blog link inside an OpenAI page and follows it).
Proposed change: Remove the 6 unlisted domains from the
network.allowedblock, or add explicit# not a primary sourcecomments so future maintainers know they're intentionally off the browse path.3. Add explicit source priority ordering (est. −5–10 AIC/run)
The four sources in Browsing Instructions are listed without priority. On days when all sources are sparse, the agent may fetch all four to triangulate. Adding an explicit fallback chain reduces this:
Proposed addition at top of Browsing Instructions:
This turns the "explore all" pattern into a "first-match" pattern, consistent with Recommendation 1.
Run-level evidence table
Tool Usage Audit
web-fetchOnly one tool configured; it is used in every run. No tool removals warranted.
Inline Sub-Agent Analysis
The workflow has 4 major sections (Research Strategy, Browsing Instructions, Selection Criteria, Output). Scoring:
No section scores ≥ 4. The fetch and selection steps are tightly coupled (selection requires seeing fetched content), and the final write is synthesis. No inline sub-agents recommended.
Summary of Estimated Savings
At avg 59.7 AIC/run, these changes could reduce spend by 28–52% on the high-variance runs, bringing the expected average closer to 40–45 AIC/run.
Caveats