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You are building a multi-agent research system using the Claude Agent SDK.

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

Your multi-agent research pipeline crashes after processing 12 of 28 documents. The web-search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun identifying patterns. You need to resume processing without repeating work or losing fidelity in the prior findings.

What state-management approach best balances information fidelity with context efficiency when restoring agent state?

A.

Index all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.

B.

Have each agent persist a structured export to a known location. On resumption, the coordinator loads the manifest and injects relevant state into agent prompts.

C.

Have each agent maintain its own persistent state file and reload it independently at the beginning of every session.

D.

Persist the coordinator’s conversation log containing all task delegations and responses, providing this log to agents when resuming.

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