Target Workflow
Daily Agentic AI Research Digest (daily-agentic-research.md)
Why selected: Third-highest AIC workflow in the repository (981 AIC / 7 runs over the analysis window). While recently analyzed, fresh run data reveals continued extreme AIC volatility (8× variance) driven by unconstrained web-fetch behavior — a structurally different problem from prior optimization passes. The AIC monitoring family workflows were excluded from consideration as higher priorities due to 7-day recency.
Analysis Period
7 runs from 2026-06-26 to 2026-07-02 (all successful, no failures).
Spend Profile
| Metric |
Value |
| Total AIC |
981.00 |
| Avg AIC / run |
140.14 |
| Min AIC / run |
39.11 (run #14, 4 turns) |
| Max AIC / run |
310.66 (run #12, ~20 turns est.) |
| AIC variance ratio |
8× |
| Avg turns / run (5 known) |
9.2 |
| Avg tokens / turn |
~27,050 |
| AIC / 1,000 tokens |
~0.35 |
Per-run breakdown (7 runs)
Token estimates for runs 11–12 derived from confirmed AIC-per-token ratio (0.351 AIC/1,000 tokens) across 5 measured runs.
Key Finding: AIC Cost Is Turn-Linear
AIC tracks nearly linearly with turn count at ~9.45 AIC/turn. The 8× AIC variance (39 to 311) is driven entirely by turn count variance (4 to ~20), not model choice or prompt size. The agent consumes ~27,000 tokens/turn regardless of run — meaning every extra source fetch adds ~10 AIC.
The two most expensive runs (Jun 27–28, combined 544 AIC = 55% of weekly total) likely triggered 3+ source fetches including the broad arxiv search URL, which returns large result pages.
Ranked Recommendations
1. Harden the source-fetch limit and early-exit condition — estimated ~55 AIC/run savings
Current prompt (§ Browsing Instructions):
Browse 2–3 of these sources (don't fetch all if you find a strong candidate early).
Problem: "2–3" is permissive, and "if you find a strong candidate" is vague. On Jun 27 and Jun 28 the agent consumed 17–20 estimated turns (234–311 AIC). Capping at 2 sources and making the early-exit binary would eliminate these outlier runs.
Proposed change:
Fetch at most 2 sources. After each fetch, stop immediately if you find **any** paper or announcement published within the last 3 days — do not fetch a second source in that case.
Evidence: The cheapest run (Jun 30, 4 turns, 39 AIC) terminated early. The two most expensive (combined 544 AIC) did not. A hard-stop rule — not a suggestion — removes the ambiguity that causes overrun.
Estimated savings: Eliminating the two outlier runs and trimming 1–2 turns from mid-range runs projects to ~50–60 AIC/run reduction (36–43%).
2. Cap per-page content consumption — estimated ~18 AIC/run savings
Problem: At ~27,000 tokens/turn, the agent is consuming full pages including boilerplate, navigation chrome, and many result entries. The arxiv search URL alone can return 10+ full abstracts.
Proposed addition to §Browsing Instructions:
After fetching each page, extract only the first 6,000 characters. Do not scroll or paginate.
Evidence: The low-AIC run (Jun 30, 110,382 tokens across 4 turns = 27,596 tokens/turn) used the same pages. A 6,000-character cap per fetch would roughly halve per-turn token consumption, saving ~2 equivalent turns of AIC per run.
References:
- §28319198943 — highest-AIC run, agent artifact 1.84 MB vs 0.80 MB for recent run
- §28438420177 — most efficient run, 4 turns / 110,382 tokens
3. Replace broad arxiv search URL with max-results API endpoint — estimated ~10 AIC/run savings (when arxiv is fetched)
Current: `(arxiv.org/redacted)
This returns a full HTML search page with 10 results and full abstracts. The export API (export.arxiv.org — already allowlisted) returns structured Atom XML with a configurable result cap.
Proposed replacement:
(export.arxiv.org/redacted)
Evidence: export.arxiv.org is already in the network allowlist. Limiting to 3 results removes 70% of arxiv page content on fetches where this source is used. Conservative savings of ~1 turn equivalent (~10 AIC) per run where arxiv is visited.
Structural Optimization — Inline Sub-Agent for Browse-and-Extract
The workflow has no existing ## agent: blocks and has 4 major ## sections. The §Browsing Instructions section is a strong sub-agent candidate.
Candidate: browse-sources sub-agent
| Dimension |
Score |
Rationale |
| Independence |
2/3 |
Can run before selection/output phases |
| Small-model adequacy |
3/3 |
Pure extraction: find title, date, abstract, URL from HTML — no synthesis |
| Parallelism |
1/2 |
Sequential browsing (early-exit pattern discourages parallel fetches) |
| Size |
2/2 |
Substantial: 2+ web fetches, multi-page scanning |
| Total |
8/10 |
Strong candidate |
Why a smaller model fits: The browsing phase is entirely extractive — the agent matches paper titles against a date filter and extracts structured metadata. No judgment or synthesis is needed. A smaller model can identify "this paper is from July 1, here is its title and abstract" as reliably as the main model.
Proposed invocation change: Add a new ## agent: browse-sources block before ## Selection Criteria:
## agent: browse-sources
Fetch at most 2 of the following sources. For each page, extract all papers or announcements published in the last 7 days. Return a JSON array of objects with keys: title, url, date, one_sentence_summary. Stop after the first source if you find any item from the last 3 days.
Sources (priority order):
1. (huggingface.co/redacted)
2. (openai.com/redacted)
Respond with only the JSON array — no prose.
The main agent then receives the extracted candidates and applies the §Selection Criteria + §Output steps without reading raw HTML. This shifts ~60–70% of turn costs to a cheaper model.
Estimated savings: 30–50 AIC/run, depending on model cost differential. Conservative: ~30 AIC/run.
Caveats
- Token estimates for runs 11–12 (Jun 27–28) are derived from the consistent AIC-per-token ratio (0.351) across 5 measured runs; actual figures may differ.
- The arxiv export API returns Atom XML rather than HTML; the agent prompt may need to be updated to mention this format if it causes parsing confusion.
- The sub-agent recommendation depends on the agentic workflow framework's inline sub-agent support and the cost tier of the smaller model available.
- Recommendations 1–3 are independent and safe to apply individually.
Generated by Agentic Workflow AIC Usage Optimizer · 345.4 AIC · ⊞ 21.6K · ◷
Target Workflow
Daily Agentic AI Research Digest (
daily-agentic-research.md)Why selected: Third-highest AIC workflow in the repository (981 AIC / 7 runs over the analysis window). While recently analyzed, fresh run data reveals continued extreme AIC volatility (8× variance) driven by unconstrained web-fetch behavior — a structurally different problem from prior optimization passes. The AIC monitoring family workflows were excluded from consideration as higher priorities due to 7-day recency.
Analysis Period
7 runs from 2026-06-26 to 2026-07-02 (all successful, no failures).
Spend Profile
Per-run breakdown (7 runs)
Token estimates for runs 11–12 derived from confirmed AIC-per-token ratio (0.351 AIC/1,000 tokens) across 5 measured runs.
Key Finding: AIC Cost Is Turn-Linear
AIC tracks nearly linearly with turn count at ~9.45 AIC/turn. The 8× AIC variance (39 to 311) is driven entirely by turn count variance (4 to ~20), not model choice or prompt size. The agent consumes ~27,000 tokens/turn regardless of run — meaning every extra source fetch adds ~10 AIC.
The two most expensive runs (Jun 27–28, combined 544 AIC = 55% of weekly total) likely triggered 3+ source fetches including the broad arxiv search URL, which returns large result pages.
Ranked Recommendations
1. Harden the source-fetch limit and early-exit condition — estimated ~55 AIC/run savings
Current prompt (§ Browsing Instructions):
Problem: "2–3" is permissive, and "if you find a strong candidate" is vague. On Jun 27 and Jun 28 the agent consumed 17–20 estimated turns (234–311 AIC). Capping at 2 sources and making the early-exit binary would eliminate these outlier runs.
Proposed change:
Evidence: The cheapest run (Jun 30, 4 turns, 39 AIC) terminated early. The two most expensive (combined 544 AIC) did not. A hard-stop rule — not a suggestion — removes the ambiguity that causes overrun.
Estimated savings: Eliminating the two outlier runs and trimming 1–2 turns from mid-range runs projects to ~50–60 AIC/run reduction (36–43%).
2. Cap per-page content consumption — estimated ~18 AIC/run savings
Problem: At ~27,000 tokens/turn, the agent is consuming full pages including boilerplate, navigation chrome, and many result entries. The arxiv search URL alone can return 10+ full abstracts.
Proposed addition to §Browsing Instructions:
Evidence: The low-AIC run (Jun 30, 110,382 tokens across 4 turns = 27,596 tokens/turn) used the same pages. A 6,000-character cap per fetch would roughly halve per-turn token consumption, saving ~2 equivalent turns of AIC per run.
References:
3. Replace broad arxiv search URL with max-results API endpoint — estimated ~10 AIC/run savings (when arxiv is fetched)
Current: `(arxiv.org/redacted)
This returns a full HTML search page with 10 results and full abstracts. The export API (
export.arxiv.org— already allowlisted) returns structured Atom XML with a configurable result cap.Proposed replacement:
Evidence:
export.arxiv.orgis already in the network allowlist. Limiting to 3 results removes 70% of arxiv page content on fetches where this source is used. Conservative savings of ~1 turn equivalent (~10 AIC) per run where arxiv is visited.Structural Optimization — Inline Sub-Agent for Browse-and-Extract
The workflow has no existing
## agent:blocks and has 4 major##sections. The §Browsing Instructions section is a strong sub-agent candidate.Candidate:
browse-sourcessub-agentWhy a smaller model fits: The browsing phase is entirely extractive — the agent matches paper titles against a date filter and extracts structured metadata. No judgment or synthesis is needed. A smaller model can identify "this paper is from July 1, here is its title and abstract" as reliably as the main model.
Proposed invocation change: Add a new
## agent: browse-sourcesblock before## Selection Criteria:The main agent then receives the extracted candidates and applies the §Selection Criteria + §Output steps without reading raw HTML. This shifts ~60–70% of turn costs to a cheaper model.
Estimated savings: 30–50 AIC/run, depending on model cost differential. Conservative: ~30 AIC/run.
Caveats