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Why Do Agent Teams Fail Across Boundaries? A Failure Taxonomy for Agent-to-Agent Systems with Private States

arXiv 2610.19342 ↗First posted 14 Oct 2026cs.MA cs.AIcited by 0

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Abstract (arXiv)

Agents are increasingly interacting with other agents that act for different principals (people or organizations on whose behalf they work), such as a buyer's personal agent negotiating with an enterprise's seller agent. In such agent-to-agent (A2A) scenarios, each agent has its own environment and holds information and authority that the other cannot see. Moreover, the interests of each agent may be aligned (cooperative), partly aligned (mixed-motive), or opposed (competitive). Existing multi-agent system failure studies assume one orchestrator, shared workspace, or single principal. We ask how A2A systems fail when these assumptions are removed. We study more than 2,500 recorded runs of A2A systems built from different agent harnesses and models, spanning 488 scenarios across eight families (legal, CRM, security, commerce, etc.). From these traces, we derive A2-Failures, a taxonomy of 20 A2A failure modes in four categories: (i) discovery and delegation, (ii) grounding and record consistency, (iii) verification, and (iv) conduct and fidelity. Three findings hold across every harness, model, and scenario studied: (1) discovery and interaction are driven by the phrasing of the task rather than the availability of peer agents; (2) no agent verifies a peer's factual claim on its own initiative, so an A2A system inherits the reliability of its least reliable participant unless the protocol or mechanism performs the check; and (3) the aggregate outcome score credits the agent completing the task while staying blind to the coordination, verification, and conduct failures beneath it, so A2A systems must be evaluated per agent, against each agent's own principal. We release the run logs behind the taxonomy at https://huggingface.co/datasets/a2a-salesforce/a2-failures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 58 references that are in the map. First posted on arXiv 14 Oct 2026.

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References · 25

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
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  10. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  11. 2024Frontier Models are Capable of In-context Schemingcited by 287
  12. 2023Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theorycited by 230
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  3. 2025A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)cited by 101
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  5. 2026MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarkscited by 32
  6. 2025Echoing: Identity Failures when LLM Agents Talk to Each Othercited by 9
  7. 2026The Illusion of Multi-Agent Advantagecited by 9
  8. 2025CREW-WILDFIRE: Benchmarking Agentic Multi-Agent Collaborations at Scalecited by 7
  9. 2025ProtocolBench: Which LLM MultiAgent Protocol to Choose?cited by 6
  10. 2026MAS-ProVe: Understanding the Process Verification of Multi-Agent Systemscited by 6
  11. 2026EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?cited by 4
  12. 2026Counterparty Modeling is Not Strategy: The Limits of LLM Negotiatorscited by 2
  13. 2026Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mindcited by 1

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