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The paper investigates how AI agents that run long workflows using external tools can experience inconsistencies when retries, speculative execution, concurrency, or partial failures occur. It introduces an effect‑history model that distinguishes between actual external events and the agent’s observations, and catalogs eight common external‑effect anomalies. The authors analyze the standard Model Context Protocol tool interface, finding that its annotations are too coarse to fully express the necessary capabilities to prevent these anomalies, thereby motivating the need for reusable transactional contracts at the agent‑tool boundary.
arXiv:2606. 09084v1 Announce Type: cross Abstract: Tool-using LLM agents interact with the world through actions that persist state in artifacts (e.
arXiv:2606. 30531v1 Announce Type: new Abstract: Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task.
The paper audits silent failures in agent-to-tool interactions within the ToolUniverse environment, focusing on 15 scientific tools. It identifies 91 failures—primarily missing data or inconsistent search/filtering—occurring mainly in the API and wrapper layers, and shows how these silent failures can propagate downstream into seemingly valid outputs. The authors propose contextual reliability and recommend testing, disclosure, monitoring, and measurement strategies for the agent-tool pipeline.
arXiv:2606. 29073v1 Announce Type: cross Abstract: Model Context Protocol (MCP)-style ecosystems give language-model applications a practical connection layer for tools, resources, prompts, and transports.
arXiv:2607. 11098v1 Announce Type: cross Abstract: Tool-using LLM agents are mostly evaluated assuming all tools work.