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Iterative Multi-Agent Brainstorming

Spawns several agents in parallel on the same problem, often with different perspectives, then merges their ideas into one set of options

experimental but awesome Updated 2026-10-13 By Nikola Balic (@nibzard) Based on Boris Cherny (via Claude Code capability)
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Cite This Pattern
APA 
Nikola Balic (@nibzard) (2026). Iterative Multi-Agent Brainstorming. In *Awesome Agentic Patterns*. Retrieved October 14, 2026, from https://agentic-patterns.com/patterns/iterative-multi-agent-brainstorming
BibTeX 
@misc{agentic_patterns_iterative-multi-agent-brainstorming,
  title = {Iterative Multi-Agent Brainstorming},
  author = {Nikola Balic (@nibzard)},
  year = {2026},
  howpublished = {\url{https://agentic-patterns.com/patterns/iterative-multi-agent-brainstorming}},
  note = {Awesome Agentic Patterns}
}

Use when

  • Task needs a wide range of ideas or solution approaches
  • A single agent keeps converging on the same answer
  • A coordinator agent or human can synthesize the outputs

Avoid when

  • Task has one clear correct answer
  • Budget cannot cover several parallel agent runs

Problem

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.

Solution

Employ a multi-agent approach for brainstorming and idea generation. This involves:

  1. Defining a core problem or task.
  2. Spawning multiple independent (or semi-independent) AI agent instances.
  3. Assigning each agent the same initial task or slightly varied perspectives on the task.
  4. Allowing each agent to work in parallel to generate ideas, solutions, or approaches.
  5. Collecting the outputs from all agents.
  6. 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.

Example (parallel brainstorming)

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]

Example

  • "Use 3 parallel agents to brainstorm ideas for how to clean up @services/aggregator/feed_service.cpp." (from Claude Code examples)

How to use it

  • Use this when you need diverse perspectives or want to avoid local optimum trapping.
  • Assign distinct roles or perspectives to each agent (e.g., critic, optimist, technical realist).
  • Limit to 2-4 agents for manageable coordination (practitioner guidance, not a measured threshold); note that research on sampling-and-voting finds accuracy generally improves with more agents, so add agents when the task rewards coverage and you can afford the runs.
  • Use a coordinating agent or human to synthesize and deduplicate outputs.

Trade-offs

  • Pros: Explores wider solution space, reduces local optimum trapping, enables diverse perspective exploration.
  • Cons: Adds orchestration complexity, coordination overhead increases with agent count, requires synthesis mechanisms.

References

Source