4 Parallel Agents, 102 Tool Calls: Automating Naver Algorithm Research with Claude Code
Three dental ad clients asked the same question in the same week: “Naver feels different lately — did the algorithm change?” The ADVoost rollout had shifted how results behaved, and the feedback kept coming. “It seems like it changed” isn’t a useful answer. 6 sessions and 102 tool calls later, I had analyzed 17 official Naver announcements and produced 3 HTML reports.
TL;DR: Used the research skill to dispatch 4 parallel agents → Codex cross-verification to isolate unconfirmed claims → iterative HTML report generation. Bonus: caught the Naver AI Briefing ad beta launch in real time, one day after it went live on 2026-05-07.
4 Opus Agents Simultaneously: 4 Research Angles in 21 Minutes
Single-prompt research on Naver algorithm changes doesn’t work. Official announcements, industry community observations, ADVoost technical changes, dental ad practice implications — these four angles pull from different sources with different reliability levels. Mix them in one prompt and the model blends verified facts with speculation.
The research skill’s solution: dispatch 4 agents in parallel from a single message.
Agent 1: Official announcement extraction & Search Advisor docs
Agent 2: Organic search & Place ranking changes (community + industry observation)
Agent 3: ADVoost matching logic analysis
Agent 4: Dental/medical ad practical implications
Each agent prompt required: “source URL mandatory, flag overlap with sibling agents.” Once 4 independent output files landed, a separate synthesis session extracted agreements, contradictions, and unverified claims. Work that would’ve taken 80 minutes sequentially finished in 21.
Session 1 tool calls: Agent(8), Read(5), Bash(4), TodoWrite(3), Write(2).
The Unverified Announcement Codex Caught — Evidence Grading Introduced
The problem surfaced immediately when combining all 4 agent outputs. The first draft included a “2026-05 Place Ad policy announcement” stated as fact — but it appeared in none of the 15 entries in naver_ads_notice_extracts.json. In medical advertising, stating “algorithm changes” without official confirmation is a compliance liability.
Session 2 created a standalone claude_synthesis_review.md and enforced five evidence tiers:
CONFIRMED → explicitly stated in Naver Ads notices
(broad match, ADVoost, medical content rules, Place ad test)
INFERRED → reasonably deducible from official help docs
OBSERVED → community/practitioner reports, no official notice
EXTRAPOLATED → logical reasoning from confirmed data
UNVERIFIED → no source or unconfirmed claim
Organic search and general Place ranking are undisclosed by Naver policy — they couldn’t be elevated above OBSERVED. Without this explicit grading step, unconfirmed claims would’ve made it into the client report unchanged.
Session 2 tool calls: Read(4), Bash(4), Write(2), Grep(1). Elapsed: 5 minutes.
API Overload and Why File-Based State Management Actually Matters
Session 3 hit API Error: Overloaded attempting the first HTML report generation. Opus 4.7 with 8 parallel agents was too many concurrent requests.
No work was lost. research-minutes.md and claude_naver_research_report.md were already written to disk. Session 4 picked up immediately and moved straight to report generation. This is the practical payoff of file-based state management: when agents write intermediate results to files, a broken session doesn’t lose context.
Session 4 produced naver_algorithm_ad_agency_prediction_report_2026-05-08.html — 40.9KB, 429 lines. Structure:
§1 Key conclusions + medical ad safety box (pinned to top)
§2 Evidence tier legend (5-level visualization)
§3 Objective data — metric cards from 17 announcements
§4 Organic search & Place observations
§5 Ad agency impact predictions
§7 Dongbaek Place Ad pilot analysis
Session 4 tool calls: Bash(15), Read(6), Grep(4), Glob(1), Write(1). Bash ran 15 times because each Write was followed by script-based validation — tag balance, DOCTYPE presence, viewport meta, section existence.
The client then requested “last 6 months only, essentials.” Session 5 produced a third version. Three HTML reports across three sessions wasn’t scope creep — requirements evolved: “overall changes” → “agency impact predictions” → “last 6 months compressed.”
The Daily Agent That Caught Yesterday’s AI Briefing Beta
Session 6 produced an unexpected result. Running the daily research agent defined in medical_dental_ads_daily_goal.md, one of the official source scans fired a hit.
2026-05-07 — the day before — Naver launched an ‘AI Briefing’ ad beta.
The key was explicit target URLs in the agent prompt:
- Naver Ads Notice: https://ads.naver.com/notice?categoryId=147
- Naver Ads Help: https://ads.naver.com/help
- Naver Search Advisor: https://searchadvisor.naver.com/
- Korea Dental Ad Review: https://www.dentalad.or.kr
Without this list, generic WebSearch prioritizes blog posts. Specifying URLs directly changes the source reliability floor.
WebSearch(9) + WebFetch(6) confirmed:
- Phase 1: Shopping search ads/ADVoost integration, shown below AI-generated answer summaries
- Healthcare vertical AI: explicitly on the in-year roadmap
- Medical keyword coverage: [UNVERIFIED]
Without the daily agent, this would’ve surfaced days later. Instead it went directly into that day’s client briefing.
Full Stats
| Tool | Calls | Primary use |
|---|---|---|
| Bash | 29 | File validation, state updates |
| Read | 28 | Source file reads |
| Write | 9 | Output file generation |
| WebSearch | 9 | Official source discovery |
| Agent | 8 | Parallel research dispatch |
| WebFetch | 6 | Announcement page crawling |
| Grep | 5 | Keyword verification |
| TodoWrite | 3 | Step tracking |
| Total | 102 |
8 files created, 0 files modified. Each iteration produced a new file rather than overwriting — overwriting intermediate results removes the comparison baseline for Codex cross-verification.
Parallel agents work well for breadth problems. 4 simultaneous agents outperform 4 sequential runs on both speed and coverage balance. Evidence grading has to be an explicit step — without a dedicated classification pass, verified facts and inferences merge. Moving the grading to a separate session and running Codex cross-verification measurably improved the final report quality.
More projects and build logs at jidonglab.com
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