The paper introduces the Agent Transfer Protocol (ATP), a cross‑organization communication protocol designed to enable continuous collaboration among agents from different administrative domains. ATP uses domain‑mediated servers to maintain stable addresses, store messages for offline instances, and verify message integrity, allowing agents to operate independently of continuous connectivity. A prototype implementation demonstrated that business workflows could continue after instances went offline, with successful verification of message source and content and quantified latency overhead.
arXiv:2606. 19135v1 Announce Type: cross Abstract: As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks.
arXiv:2608. 03214v1 Announce Type: new Abstract: Large language models have transformed artificial intelligence from isolated prediction services into components of long-running, distributed systems that reason, invoke tools, retrieve external state, delegate tasks, and act on behalf of users and organizations.
arXiv:2606. 09751v1 Announce Type: new Abstract: Foundation models are moving from response generation into operational roles.
arXiv:2610.15459v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) use specialized agents that exchange messages to accomplish complex tasks. Their reliance on networked protoc...
arXiv:2606. 19795v1 Announce Type: cross Abstract: Electronic design automation (EDA) is inherently multi-stage and handoff-heavy.
The Agent Status Protocol (ASP) is a lightweight specification that enables heterogeneous AI agents to report their operational state to a status server via small JSON events over HTTP POST. The server aggregates these reports and exposes the combined state to observers through Server‑Sent Events (SSE), inferring liveness from missed heartbeats and incorporating observations from supervisors, guardrails, operators, and peers. ASP models state across independent dimensions—lifecycle, health, task, floor, governance—allowing an agent to simultaneously be online, degraded, working on a task, and waiting for the floor, while reusing existing vocabularies from A2A, OFP, and OpenTelemetry GenAI conventions.
arXiv:2608. 13574v1 Announce Type: new Abstract: LLM agents increasingly operate as execution systems that invoke tools, modify local state, use persistent memory, and interact with external protocols.
arXiv:2607. 16200v1 Announce Type: new Abstract: AI agent systems that couple large language models (LLMs) with external tools and APIs are inherently non-deterministic: LLM sampling variance, external API state, CDN infrastructure headers, and execution-environment noise collectively prevent any prior agent run from being faithfully re-executed.
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:2606. 30317v1 Announce Type: cross Abstract: The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services.
arXiv:2605. 24248v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a host reads a server's self-declared tool list and dispatches calls, with no notion of which servers it may use, at what sensitivity, or which of a server's tools are in bounds.
arXiv:2609. 00546v1 Announce Type: cross Abstract: Agent systems are commonly described by the model and harness that currently produce their behavior.