Agent Coordinator

Agent Coordinator

Distilled from Claude Code Team mode (TeamCreate/SendMessage protocol). Coordinates multiple AI agents to work on tasks in parallel, then aggregates results.

When to use

• Parallel data collection (scrape multiple sources)
• Parallel analysis (analyze multiple stocks/documents)
• Task decomposition (break complex task into subtasks)
• Review pipeline (one agent writes, another reviews)
• Ensemble decision (multiple agents vote on a decision)

How it works

1. Task Decomposition — Break a complex task into parallel subtasks
2. Agent Dispatch — Assign each subtask to an agent worker
3. Parallel Execution — Run agents concurrently
4. Result Aggregation — Collect and merge results
5. Conflict Resolution — Handle conflicting outputs

Usage

`ash

Decompose and run a complex task

python3 {baseDir}/coordinator.py --task "Analyze top 5 tech stocks" --workers 3

Run with custom subtasks

python3 {baseDir}/coordinator.py --subtasks subtasks.json

Review pipeline (one writes, another reviews)

python3 {baseDir}/coordinator.py --pipeline review --input report.md

Ensemble decision

python3 {baseDir}/coordinator.py --ensemble "Should we buy?" --voters 3

Export results

python3 {baseDir}/coordinator.py --task "Research AI trends" --export results.json `

Task format

json [ {"id": "subtask-1", "description": "Analyze AAPL financials", "agent": "analyst"}, {"id": "subtask-2", "description": "Analyze GOOGL financials", "agent": "analyst"}, {"id": "subtask-3", "description": "Compare results", "agent": "reviewer"} ]

Built-in coordination patterns

Algorithm reference

Based on Claude Code Team mode:

• Task queue with priority
• Agent worker pool with concurrency limit
• Result aggregation with conflict resolution
• Timeout per subtask
• Progress tracking