dispatching-parallel-agents

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Evaluate when to dispatch parallel agents; use a 4-step pattern to split, coordinate, and integrate results.

AI & Automation 104 stars 25 forks Updated today MIT

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Skill Content

# Dispatching Parallel Agents ## Overview Parallel agents are suitable for handling splittable tasks with low coupling. The goal is to shorten total lead time, not to increase coordination overhead. ## When to Use - Tasks can be decomposed into independent sub-problems (e.g., documentation, testing, separate modules). - Dependencies between sub-tasks are low; clear interfaces can be defined. - Deadlines are tight, and lead time needs compression. ## When NOT to Use - Problems are highly coupled and require frequent synchronization. - Requirements are still unclear; the cost of decomposition outweighs the benefits. - Unable to define unified acceptance criteria. ## Four-Step Pattern 1. **Decompose**: Split into mutually exclusive, independently verifiable sub-tasks. 2. **Dispatch**: Specify each agent's scope, inputs/outputs, constraints, and deadline. 3. **Sync**: Perform brief synchronizations at fixed checkpoints to handle conflicts and dependencies. 4. **Merge**: Integrate outputs using a unified set of acceptance criteria and run regression tests. ## Best Practices - Every sub-task MUST have a clear Definition of Done. - Collaborate primarily via interface contracts to avoid modifying each other's blocks. - Perform a global check before final integration. ## Common Mistakes - Forcefully splitting non-splittable tasks, leading to rework. - Lacking sync checkpoints, resulting in integration explosion at the end. - Sub-tasks passing individually but failing globa...

Details

Author
KbWen
Repository
KbWen/agentic-os
Created
3 months ago
Last Updated
today
Language
Python
License
MIT

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