When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems

Jinhao Zhou; Xiaoyu Zhang; Jiongchi Yu; Fahao Chen; Jun Wu

cs.SEOct 11, 2026

When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems

Authors: Jinhao Zhou, Xiaoyu Zhang, Jiongchi Yu, Fahao Chen, Jun Wu

Organizations: Waseda University, Japan · The Hong Kong University of Science and Technology, Hong Kong SAR, China · Nanyang Technological University, Singapore · Shandong University, China ·

Abstract

LLM-based multi-agent systems (LLM-MAS) use specialized agents that exchange messages to accomplish complex tasks. Their reliance on networked protocols, remote tools, and model endpoints exposes communication to message loss, delay, and context expiration. Because agents often exchange open-ended natural language, anomalies can alter task-relevant information while leaving messages processable, allowing semantic errors to propagate through subsequent interactions. Systematic studies of faults in inter-agent communication remain limited. This paper presents a large-scale empirical study of how communication faults manifest, propagate, and affect task outcomes in LLM-MAS. We construct a cross-layer taxonomy linking lower-stack disturbances to application-visible faults and develop a fault-injection and tracing pipeline. Our evaluation covers more than 29,000 executions across 100 tasks, five domains, three workflow topologies, and three model configurations. We find that 20.7% of failures with complete detection records occur without an immediate failure signal, reaching 76.2% for partial-content faults. Flat maintains 53.0% task success under communication faults, compared with 3.3% and 3.2% for Sequential and Hierarchical, respectively. This advantage primarily arises when an alternative information path preserves the evidence required by downstream agents. We also evaluate execution-side and evidence protection mechanisms and find that they recover different failure cases. Evidence recovery depends on whether trustworthy information remains accessible through an unaffected path. These observations provide empirical insights into communication-fault propagation, workflow resilience, and protection design for LLM-MAS.

Figures & tables

Figure 1. A silent partial-content communication failure in an LLM-MAS. Although the message remains syntactically valid, the Verifier accepts incomplete evidence without an immediate failure signal.

Figure 2. Cross-layer communication-fault propagation. P/D/N/T denote conventional lower-stack communication layers; A represents application-level inter-agent communication and receiver-visible communication faults; M and O represent downstream MAS consequences and task outcomes.

Layer	Fault / effect name	Brief description	Representative source(s)
P	P-F1 Bit-level corruption	Isolated or burst errors in communicated bits.	( Stone and Partridge, 2000 )
	P-F2 Link/endpoint interruption	NIC, cable, or power disruption removes a path.	( Braden, 1989 ; Linux Kernel Documentation, 2011 )
	P-F3 Physical-medium interference	Interference degrades reception and delivery timing.	( Karn et al., 2004 )
	P-F4 Timing instability	Clock drift affects communication timing decisions.	( Mills et al., 2010 )
	P-F5 Communication-state corruption	Hardware errors affect communication buffers or runtime state.	( Carreira et al., 1998 ; Linux Kernel Documentation, 2026 )
D	D-F1 Frame loss/local drop	Frames are not delivered across a local link.	( Fairhurst and Wood, 2002 ; Karn et al., 2004 )

Table 1. Cross-layer taxonomy and representative sources. P/D/N/T entries are lower-stack communication-source families; A entries are receiver-visible communication-fault families; and M entries are downstream MAS consequence groups. Representative sources for the underlying phenomena are listed in the final column.

Figure 3. AutoGen workflows and injection boundaries used in the confirmation matrix. Sequential uses one evidence path, Flat adds an alternate evidence route, and Hierarchical aggregates evidence through a supervisor. The injection boundaries are action requests, observations, and evidence handoffs.

Figure 4. Cross-layer manifestation and failure visibility under communication faults. The left panel shows the distribution of primary application-level manifestations across 3,600 faulted source-mapping executions. The right panel shows silent-failure rates among 8,335 failures with complete detection records. Lower-stack fault sources exhibit many-to-many mappings to application-level symptoms, while silent failures concentrate in partial-content and semantic-corruption faults.

Figure 5. Representative silent failure under partial-content delivery. The message remains parseable after required evidence is removed, and the Verifier accepts it without recorded detection or recovery.

Model	Topology	I2 (%)	I3 (%)	I4 (%)
DeepSeek	Sequential	90.7	72.2	0.0
	Hierarchical	94.6	62.5	0.0
	Flat	87.0	75.9	94.4
Qwen3.5-9B	Sequential	72.4	75.9	0.0
	Hierarchical	75.0	75.0	0.0
	Flat	75.0	78.6	96.4

Table 2. Matched task-success rates under non-delivery at I2, I3, and I4 in Admin. Each model uses the same matched task-repeat cohort across the three communication stages.

Figure 6. Representative impact of non-delivery at different communication stages. At I2, request loss prevents the Worker from producing the required evidence. At I4, the evidence already exists and remains available through the Flat topology’s additional path.

Figure 7. Task-failure rates under semantic corruption and partial-content faults across five task contexts with the Flat topology.

Figure 8. Impacts of topology and model configuration under communication faults. (a) Clean-conditional loss across three topologies and three model configurations. Error bars report task-clustered 95% confidence intervals. (b) Paired success advantage of Flat over Sequential and Hierarchical under five fault conditions. Flat gains concentrate at the evidence handoff stage, where its branch carries the required evidence.

Figure 9. Representative topology-dependent outcome under the same I4 non-delivery fault. Sequential and Hierarchical lose their only downstream evidence path, whereas Flat preserves the required evidence through its additional direct branch.

Figure 10. Guarded evidence recovery repairs incomplete evidence without repeating the completed action.

Figure 11. Success rates under the main protection study. Execution-side protection primarily recovers execution faults, whereas evidence protection recovers information faults.

Fault scope	Sequential	Flat
Initial handoff	$0\to 18$	$18\to 18$
Handoff and recovery deliveries	$0\to 0$	$18\to 18$
All evidence paths	$0\to 0$	$0\to 0$

Table 3. Recovery boundary under repeated fault exposure. Entries show Baseline

\rightarrow

Combined successful runs.

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