LAP: An Agent-to-Instrument Protocol for Autonomous Science

• URL: http://arxiv.org/abs/2606.03755v1
• Date: Tue, 02 Jun 2026 15:03:43 GMT
• Title: LAP: An Agent-to-Instrument Protocol for Autonomous Science
• Abstract summary: Large language model agents now plan experiments, and self-driving laboratories execute them.<n>Every such system rebuilds the link between the reasoning agent and the physical instrument from scratch.<n>Recent agent-interoperability protocols clarify the agent-to-tool edge, and Google's Agent2Agent (A2A) the agent-to-agent edge.<n>We present the Lab Agent Protocol (LAP), a protocol that fills this gap.
• Score: 6.00746946741668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous science is moving from demonstration to infrastructure. Large language model agents now plan experiments, and self-driving laboratories execute them. Yet every such system rebuilds the link between the reasoning agent and the physical instrument from scratch, against fragmented vendor SDKs and standards built for deterministic software clients rather than probabilistic, goal-directed agents. Recent agent-interoperability protocols clarify two of the three edges of an agentic ecosystem (Anthropic's Model Context Protocol (MCP) standardizes the agent-to-tool edge, and Google's Agent2Agent (A2A) the agent-to-agent edge), but neither models the agent-to-instrument edge, where operations are stateful, safety-critical, exclusively owned, physically embodied, and produce measurements with units, calibration, and uncertainty. We present the Lab Agent Protocol (LAP), a protocol design that fills this gap. LAP retains A2A's peer-to-peer, discovery-first, task-lifecycle structure and adds four physical-world primitives: (i) the InstrumentCard, a signed capability and physical-limit description; (ii) first-class reservation for exclusive instrument and sample locking; (iii) a safety-fence handshake with operator-confirmation tokens cryptographically bound to a specific task and its parameters, gating hazardous and irreversible operations; and (iv) a MeasurementResult schema that makes every result physically typed (QUDT/UCUM), calibration-anchored, uncertainty-bearing, and reproducible by construction. We specify roles, a six-layer architecture, the JSON-RPC method set, the task and safety state machines, the error model, and cross-laboratory federation, and walk a closed-loop autonomous campaign through the protocol end-to-end. LAP is transport-compatible with the A2A/MCP ecosystem and encapsulates rather than replaces existing device standards such as SiLA 2 and OPC-UA.

Related papers

• A2ABreak: Systematic Security Analysis of the A2A Protocol [9.340181893879683]
  This paper presents A2ABreak, the first rigorous systematic security analysis of the A2A protocol.<n>We introduce a novel framework that utilizes an LLM-assisted extraction of a verified finite-state machine directly from the natural-language specification.<n>Our analysis uncovers 11 new vulnerabilities, each exploitable by a specification-compliant adversary without requiring any implementation flaw.
  arXiv  Detail & Related papers  (2026-09-09T22:18:36Z)
• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• A Technical Taxonomy of LLM Agent Communication Protocols [60.76747983053368]
  This study develops a technical taxonomy to classify and analyze large language models (LLMs) agent communication protocols.<n>The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.
  arXiv  Detail & Related papers  (2026-06-17T14:45:20Z)
• Securing LLM Agents Need Intent-to-Execution Integrity [49.490963596514185]
  We argue that securing LLM agents requires defining an end-to-end correctness property that specifies when an agent's execution faithfully reflects the user's intent.<n>We observe that LLM agents are structurally analogous to compilers, where security violations correspond to mis-executions that do not preserve user intent.<n>We derive four integrity properties that must hold simultaneously: emphTool Integrity, emphInstruction Integrity, emphJudgment Integrity, and emphData Flow Integrity.
  arXiv  Detail & Related papers  (2026-05-16T12:53:31Z)
• PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
  Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
  arXiv  Detail & Related papers  (2026-05-12T04:59:47Z)
• When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI [6.1511441479993225]
  Agentic AI systems that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents introduce a threat surface that differs materially from standalone model inference.<n> Confidential computing offers a hardware-rooted alternative: Trusted Execution Environments (TEEs) isolate agent code and data from privileged system software.<n>This survey synthesizes the design space in four parts: (i) a unified taxonomy of six TEE platforms covering deployment roles and performance tradeoffs; (ii) an agent-centric threat model spanning perception, planning, memory, action, and coordination layers mapped to nine security goals; (iii) a comparative survey of CC
  arXiv  Detail & Related papers  (2026-05-04T23:09:16Z)
• Autogenesis: A Self-Evolving Agent Protocol [60.15939127351914]
  We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.<n>We present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.
  arXiv  Detail & Related papers  (2026-04-16T14:04:06Z)
• Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks [46.32011684548212]
  AI agents are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions.<n>This position paper discusses our vision for system-level defenses against indirect prompt injection attacks.
  arXiv  Detail & Related papers  (2026-03-31T17:15:46Z)
• AgentRFC: Security Design Principles and Conformance Testing for Agent Protocols [2.1352949241716352]
  Agent protocols enable autonomous agents to discover capabilities, delegate tasks, and compose services across trust boundaries.<n>Despite massive deployment, no systematic security framework for these protocols exists.<n>We present three contributions: the Agent Protocol Stack, the Agent-Agnostic Security Model, and AgentConform.
  arXiv  Detail & Related papers  (2026-03-25T00:25:02Z)
• Bridging Protocol and Production: Design Patterns for Deploying AI Agents with Model Context Protocol [0.0]
  The Model Context Protocol (MCP) standardizes how AI agents discover and invoke external tools.<n>Three protocol-level primitives remain missing: identity propagation, adaptive tool budgeting, and structured error semantics.<n>This paper identifies these gaps through field lessons from an enterprise deployment of an AI agent platform integrated with a major cloud provider's MCP servers.
  arXiv  Detail & Related papers  (2026-03-12T17:42:24Z)
• AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations [7.660803748903973]
  Multi-agent systems (MAS) promise adaptive, reasoning-driven enterprise, yet granting agents autonomous control over tools, memory, and communication introduces attack surfaces absent from deterministic pipelines.<n>We introduce AgenticCyOps, a framework built on a systematic decomposition of attack surfaces across component, coordination, and protocol layers.<n>We apply the framework to a Security Operations Center (SOC) workflow, adopting the Model Context Protocol (MCP) as the structural basis.
  arXiv  Detail & Related papers  (2026-03-10T03:15:36Z)
• AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems [52.65695508605237]
  We introduce AI-NativeBench, the first application-centric and white-box AI-Native benchmark suite grounded in Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards.<n>By treating agentic spans as first-class citizens within distributed traces, our methodology enables granular analysis of engineering characteristics beyond simple capabilities.<n>This work provides the first systematic evidence to guide the transition from measuring model capability to engineering reliable AI-Native systems.
  arXiv  Detail & Related papers  (2026-01-14T11:32:07Z)
• Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
  Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
  arXiv  Detail & Related papers  (2026-01-12T21:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.