---
title: "Motion category classification via parallel subagent pipeline"
date: 2026-06-15
category: best-practices
module: analysis/right_wing
problem_type: best_practice
component: development_workflow
severity: medium
applies_when:
  - "classifying thousands of items into policy categories using LLMs"
  - "sequential LLM batch pipelines time out or run too slowly"
  - "a classification taxonomy can be derived from a sample rather than predefined"
  - "items are independently classifiable with no cross-item state"
tags:
  - motion-classification
  - subagent-dispatch
  - parallelism
  - duckdb
  - category-taxonomy
---

# Motion category classification via parallel subagent pipeline

## Context

The `right_wing_motions` table in `data/motions.db` had a `category` column that was 100% NULL across 3,030 classified motions — blocking downstream Overton analysis that splits centrist support by policy domain. The existing `derive_categories.py` script used OpenRouter's `chat_completion_json_parallel` to classify motions in sequential batches, but consistently timed out after 10 minutes without classifying anything at scale. A different approach was needed.

## Guidance

### 1. Derive taxonomy from a sample first

Have a sub-agent read a random sample (e.g., 60 motions) and infer natural categories from the data. This produces categories grounded in the actual motion content rather than a preconceived list:

- The sample ensures categories reflect real distribution (migration-heavy, stikstof-driven, etc.)
- The sub-agent returns a concise taxonomy with descriptions for each category
- Include a catch-all "overig" category for edge cases

For this project the taxonomy yielded 10 categories: asiel/vreemdelingen, landbouw/natuur, veiligheid/justitie, zorg/gezondheid, economie, energie/klimaat, buitenland/europa, onderwijs/wetenschap, verkeer/infrastructuur, overig.

### 2. Chunk data into independent batches

Dump motions from the DB to JSON, then split into small chunks (~38 motions each) that fit comfortably within a single sub-agent's context window. Each chunk is a standalone JSON file containing motion_id, title, and body_text.

### 3. Dispatch parallel classification sub-agents

Spawn one sub-agent per chunk simultaneously (up to 80 in this case). Each receives:
- The chunk of motions to classify
- The taxonomy with category descriptions
- A strict JSON output format: `[{"motion_id": ..., "category": ..., "category_explanation": ...}]`
- An instruction to read both title and body_text before deciding on a category

All 80 agents run in parallel, finishing in minutes rather than hours.

### 4. Merge results and update the database

Collect all result files. Validate each for correct structure (some may use non-standard key names). Then update the DB:

```sql
UPDATE right_wing_motions
SET category = ?, category_explanation = ?
WHERE motion_id = ?
```

Verify by counting non-NULL rows.

### 5. Integrate into downstream analysis

Once the category column is populated, update analysis scripts and charts to use it. For the Overton QMD report this meant:
- A Plotly dropdown filter on the main centrist support chart to toggle between categories
- A category delta bar chart showing pre/post centrist support change per domain
- Quarterly domain trajectory charts for the 5 largest categories

## Why This Matters

- **Speed**: 80 parallel agents classified 3,030 motions in minutes vs. a sequential script that never finished at all
- **Simplicity**: No timeout handling, retry logic, or batch management needed — each agent is a fire-and-forget independent unit
- **Quality**: Classification is grounded in reasoning (reading title + full text), not keyword matching or vector similarity
- **Discoverability**: The derived taxonomy (10 categories) emerges naturally from the data rather than being imposed upfront

## When to Apply

- You have thousands of items needing per-item LLM processing
- Each item is independently classifiable
- The task fits in a sub-agent's context window when batched at ~30-50 items
- Parallel dispatch infrastructure is available (e.g., the `task` tool)

## Examples

The pipeline was applied to 3,030 Dutch right-wing motions. The taxonomy was derived from a 60-motion sample by a single sub-agent, then 80 parallel sub-agents classified ~38 motions each. Final distribution was: landbouw/natuur 487, economie 470, asiel/vreemdelingen 423, buitenland/europa 386, veiligheid/justitie 359, zorg/gezondheid 348, energie/klimaat 174, overig 159, verkeer/infrastructuur 138, onderwijs/wetenschap 86.

Two chunks needed minor fixes (used `category_label` / `predicted_category` instead of `category`). A quick validation script caught these before the DB update.

## Related

- `docs/solutions/best-practices/large-scale-subagent-2d-extremity-scoring-2026-06-05.md` — parallel subagent pattern for numeric extremity scoring (same infrastructure, different task)
- `analysis/right_wing/derive_categories.py` — the original sequential script that timed out
- `docs/solutions/best-practices/domain-decomposition-overton-analysis.md` — why category-split analysis matters for Overton interpretation
- `docs/solutions/best-practices/overton-narrative-architecture-2026-06-06.md` — QMD report structure that consumed the categories