Advisor Pattern
The Advisor Pattern is a design pattern in multi-agent systems where specialized agents provide recommendations, critiques, or strategic guidance to a primary Orchestrator or decision-making agent. Unlike direct execution agents, advisors do not perform the final action but influence the trajectory of the workflow through analysis and suggestion.
Core Mechanics
- Decoupling Analysis from Execution: Separates the cognitive load of evaluation from the mechanical load of task completion.
- Iterative Refinement: Advisors can review outputs from Executor agents and suggest improvements before finalization.
- Cost Efficiency: Allows the use of smaller, cheaper models for advisory roles while reserving large, expensive models (e.g., claude-fable-5) for complex reasoning or final synthesis.
Integration with Multi-Agent Systems
In complex workflows, the Advisor Pattern often works in tandem with the orchestrator-pattern. The orchestrator manages the flow, while advisors provide domain-specific insights. Recent optimizations highlight the importance of structuring these interactions to avoid redundant token usage and latency.
Strategic Fable 5 Optimization
Recent analyses indicate that inefficient use of high-capability models like claude-fable-5 often stems from treating them as monolithic processors rather than integrating them into structured multi-agent frameworks.
- Key Insight: Direct prompting of powerful models for every step is suboptimal. Instead, using a multi-agent structure with dedicated advisors reduces context window pressure and improves coherence.
- Source Integration: See Strategic Fable 5 Optimization: Multi-Agent Advisor and Orchestrator Patterns for detailed breakdowns of this optimization strategy.
- Implementation:
- Use lightweight models for initial drafting or data gathering.
- Deploy the Advisor Pattern to critique drafts using the high-capability model.
- Finalize with the orchestrator synthesizing the advised improvements.
Benefits
- Improved Accuracy: Specialized advisors reduce hallucination rates by focusing on narrow domains.
- Scalability: Easier to swap out specific advisors without redesigning the entire system.
- Transparency: Provides an audit trail of why a decision was made (via advisor notes).