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Why We Killed Our Multi-Agent Pipeline — Subbiah Sethuraman and Abhilash Asokan, ZS Associates
A fixed chain of specialist agents lost context and produced incoherent recommendations. The replacement separates deterministic detection, gives one agent end-to-end ownership, and uses subagents only for bounded investigations.
AI Engineer · Jul 23, 2026
Source Summary
ZS first split pharma analysis across signal, localization, attribution, and synthesis agents. Each could produce a plausible fact, yet repeated handoffs lost context and left **no agent owning the full conclusion**.
Practical Implication
Move deterministic work such as anomaly detection outside the LLM, then wake one reasoning agent from a queue. Let that agent launch **focused subagents** for evidence gathering while retaining judgment, and constrain investigations with a domain knowledge graph.
Agent-Ready Context
ZS first split pharma analysis across signal, localization, attribution, and synthesis agents. Each could produce a plausible fact, yet repeated handoffs lost context and left **no agent owning the full conclusion**. Move deterministic work such as anomaly detection outside the LLM, then wake one reasoning agent from a queue. Let that agent launch **focused subagents** for evidence gathering while retaining judgment, and constrain investigations with a domain knowledge graph. The reported architecture produced analyst-like work in **20–30 minutes after 50-plus turns**, versus three or four weeks for the compared human process. That is one domain-specific account, not a general benchmark for single-agent designs.
Context Map
agentdata#multi-agent#subagents#harness-engineeringUncertainty
The reported architecture produced analyst-like work in **20–30 minutes after 50-plus turns**, versus three or four weeks for the compared human process. That is one domain-specific account, not a general benchmark for single-agent designs.