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Daily Dose of Data Science | Avi Chawla | Substack

Claude offers two distinct multi-agent paradigms: sub-agents and agent teams. Sub-agents are isolated, fire-and-forget workers that run in their own context windows, report only final results to a parent, and cannot communicate with each other. Agent teams are long-running, collaborative instances with shared state, peer-to-peer communication, and dependency tracking via a shared task list. The key design principle is context-centric decomposition rather than role-based splitting. Five orchestration patterns are covered: prompt chaining, routing, parallelization, orchestrator-worker, and evaluator-optimizer. The post also warns against over-engineering with multi-agent systems and identifies three common failure modes: vague task descriptions, false verification, and runaway token costs.

Claude Subagents vs. Agent Teams

The query problem SQL was not designed to solve

Great breakdown. The isolation vs. shared state tradeoff maps to a fundamental architectural choice: do you optimize for parallelism and fault tolerance (subagents) or for coherence and coordination (agent teams)?
Subagents win when tasks are truly independent — but shared state via agent teams is underrated when tasks have ordering dependencies or need to build on each other’s context. The team model also enables better error recovery since teammates can course-correct each other.
For teams evaluating which pattern fits which task, we built Caliber — an open-source tool for benchmarking AI agents: <a href="https://github.com/rely-ai-org/caliber" target="_blank" rel="noopener nofollow">https://github.com/rely-ai-org/caliber</a>. Useful for empirically testing subagent vs. team patterns on your actual workloads before committing to an architecture.