QuestionQ73

Agentic Architecture & Orchestration

You are building a multi-agent research system with the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one that searches the web, one that analyzes documents, one that synthesizes findings, and one that generates reports. The system researches topics and produces comprehensive, cited reports.

In production, you observe the following:

  • Simple fact-checking queries (e.g., "What year was the Paris Climate Agreement signed?") traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query.
  • Complex comparative research benefits from running the full pipeline.
  • Your query distribution is diverse and continually evolving as users discover new applications.

What is the most effective approach to optimize the system for this varying query complexity?

Explanation

The Claude Agent SDK's orchestrator/coordinator-worker pattern is designed so that the coordinator agent itself reasons about each incoming query and dynamically determines which subagents are actually needed, rather than relying on a fixed set of hardcoded rules, categories, or a separately trained classifier. Static approaches — a binary fast-path/full-pipeline split, predefined pattern-to-subagent mappings, or a periodically retrained complexity classifier — all require ongoing manual maintenance and struggle to keep up with a query distribution that is diverse and continuously evolving as users find new use cases. Letting the coordinator assess query requirements at runtime and invoke only the necessary subagents (e.g., skipping document analysis and synthesis for a simple fact lookup, but running the full search-analyze-synthesize-report chain for comparative research) preserves both efficiency for simple queries and thoroughness for complex ones, and it generalizes automatically to new query types without requiring redesign of routing rules or model retraining. This mirrors Anthropic's guidance that model-driven, dynamic decision-making is preferable to rigid predefined workflows when task variety and unpredictability are high.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!