| title | status | authors | based_on | category | source | tags | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Iterative Multi-Agent Brainstorming |
experimental-but-awesome |
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Orchestration & Control |
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For complex problems or creative ideation, a single AI agent instance might get stuck in a local optimum or fail to explore a diverse range of solutions. Generating a breadth of ideas can be challenging for a sequential, monolithic process.
Employ a multi-agent approach for brainstorming and idea generation. This involves:
- Defining a core problem or task.
- Spawning multiple independent (or semi-independent) AI agent instances.
- Assigning each agent the same initial task or slightly varied perspectives on the task.
- Allowing each agent to work in parallel to generate ideas, solutions, or approaches.
- Collecting the outputs from all agents.
- Optionally, a coordinating agent or a human user can then synthesize these diverse outputs, identify common themes, or select the most promising ideas for further development.
This pattern leverages parallelism to explore a wider solution space and can lead to more creative or robust outcomes than a single agent might produce alone.
flowchart TD
A[Core Problem/Task] --> B[Agent 1: Perspective A]
A --> C[Agent 2: Perspective B]
A --> D[Agent 3: Perspective C]
B --> E[Solution Set 1]
C --> F[Solution Set 2]
D --> G[Solution Set 3]
E --> H[Coordinator/Human]
F --> H
G --> H
H --> I[Synthesized Solutions]
H --> J[Common Themes]
H --> K[Best Ideas Selected]
- "Use 3 parallel agents to brainstorm ideas for how to clean up
@services/aggregator/feed_service.cpp." (from Claude Code examples)
- Inspired by the example of using parallel agents for brainstorming in "Mastering Claude Code: Boris Cherny's Guide & Cheatsheet," section III.