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Multi-agent Prompts

Multi-agent prompting coordinates multiple language models or agents, enabling collaborative, competitive, or specialized interactions to solve complex tasks.

Key Concepts​

  • Agent Specialization: Each agent can be assigned a unique role, expertise, or perspective.
  • Collaboration and Competition: Agents may work together, debate, or critique each other's outputs.
  • Orchestration: A controller or system manages the flow of information and tasks between agents.
  • Emergent Problem Solving: Multi-agent setups can tackle tasks that are too complex for a single model.

Best Practices​

  1. Define Clear Agent Roles

    • Assign specific responsibilities or expertise to each agent.
    • Use explicit instructions to differentiate agent behaviors.

  2. Structure Interactions

    • Design turn-taking, debate, or review cycles.
    • Specify how agents should respond to or build on each other's outputs.

  3. Monitor and Mediate

    • Use a controller or moderator to resolve conflicts or synthesize results.
    • Set rules for escalation or consensus.

  4. Test for Synergy

    • Evaluate whether multi-agent setups outperform single-agent approaches.
    • Refine agent prompts and interaction protocols as needed.

Examples​

Collaborative Problem Solving​

Agent 1: Analyze the following business problem and propose a solution.
Agent 2: Review Agent 1's solution, identify potential risks, and suggest improvements.

Debate Format​

Agent A: Argue in favor of implementing a four-day workweek.
Agent B: Argue against implementing a four-day workweek.
Moderator: Summarize the strongest points from both sides and provide a balanced conclusion.

Specialized Agents​

Research Agent: Gather recent studies on renewable energy adoption.
Summary Agent: Summarize the key findings from the research.
Critique Agent: Identify gaps or limitations in the summarized research.

Common Pitfalls​

  1. Role Confusion

    • Unclear or overlapping agent instructions can lead to redundant or conflicting outputs.

  2. Escalating Complexity

    • Too many agents or interaction steps can make coordination difficult.

  3. Lack of Synthesis

    • Failing to combine or reconcile agent outputs may result in fragmented or inconclusive results.

Use Cases​

  1. Complex Decision Making

    • Simulating expert panels, committees, or multi-perspective analysis.

  2. Quality Assurance

    • Using reviewer or critic agents to catch errors or improve outputs.

  3. Creative Generation

    • Brainstorming, story writing, or design tasks with multiple creative agents.

When to Use Multi-agent Prompts​

Multi-agent prompting is ideal when:

  • Tasks benefit from multiple perspectives or expertise.
  • You want to simulate real-world group dynamics or debates.
  • Quality or creativity improves through review and iteration.

When to Consider Alternatives​

Consider other prompting techniques when:

  • The task is simple or well-defined for a single agent.
  • Coordination overhead outweighs the benefits.
  • Real-time or low-latency responses are required.

Tips for Optimization​

  1. Iterative Refinement

    • Adjust agent roles, prompts, and interaction patterns based on results.

  2. Automated Orchestration

    • Use scripts or tools to manage agent communication and workflow.

  3. Performance Monitoring

    • Track outcomes to ensure multi-agent setups deliver added value.