Information-Loss Curves Across Multi-Agent Handoff Chains
Abstract
Operators increasingly compose work as a chain of specialized LLM agents — a routing agent dispatches to a planning agent which delegates to a coding agent which calls a review agent. Each handoff is a serialization-deserialization step that loses information. We instrumented 142 handoff events across 38 chains spanning two ventures (Quantum Caddy CV pipeline + Parley AR research pipelines) and measured fact preservation at each agent boundary using a manually-curated rubric of 8 fact classes per chain. Mean per-handoff information retention was 91% at N=2, 76% at N=3, 58% at N=4, and 41% at N=5 — a roughly geometric decay consistent with independent loss probabilities at each boundary. We then evaluate four mitigation patterns (shared-state files, structured handoff envelopes, summarize-then-verify, retrieval-on-demand) and report effectiveness data for each. Structured handoff envelopes lift N=4 retention from 58% to 84% in our sample.
1. Introduction
The dominant LLM-application pattern in 2026 is no longer the single agent with a long prompt; it is the chain — a routing agent dispatches to a planner which delegates to a specialist which calls a reviewer [6][5]. The pattern has obvious appeal. Each specialist runs with a tight, well-curated context window. Specialists can be developed and tested independently. Chains can be reconfigured at runtime to match the shape of the work.
The pattern’s less-discussed cost is information loss at every handoff. Each agent receives a brief from its predecessor, internalizes it, acts, and emits a brief to its successor — a serialization-deserialization cycle that, like every such cycle in distributed systems, is lossy [1][8]. The size of the loss has not, to our knowledge, been measured empirically across an operator’s real production chains. This paper reports such a measurement.
We instrumented 142 handoff events across 38 chains spanning two ventures: Quantum Caddy, where the typical chain is APEX → AXIOM → coder → reviewer working on CV pipeline issues [11], and Parley, where chains are longer because ASR research and AR-render specialists are distinct agents [12]. We measured how many of the facts in the originating brief survived to the terminal agent’s output, and we then evaluated four mitigation patterns drawn from a combination of distributed-systems practice and recent multi-agent-LLM literature [4][7][9].
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Information-Loss Curves Across Multi-Agent Handoff Chains
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