Agent-Integrated Software: Interaction Contracts and Continuous Assurance
- URL: http://arxiv.org/abs/2609.11381v1
- Date: Thu, 10 Sep 2026 11:17:19 GMT
- Title: Agent-Integrated Software: Interaction Contracts and Continuous Assurance
- Abstract summary: We argue that dependable integration requires an explicit correspondence between task-level interaction and application behavior.<n>We introduce Agent-Integrated Software (AIS) as a software pattern combining a conventional core, direct interaction, and a built-in agent.<n>This perspective develops a research agenda spanning application abstraction, development support, controlled execution, quality assessment, and human supervision.
- Score: 14.374598285839275
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Embedding an intelligent agent in an existing application creates a persistent coordination problem: users can revise goals and manipulate shared objects while delegated execution continues. We argue that dependable integration requires an explicit correspondence between task-level interaction and application behavior. We introduce Agent-Integrated Software (AIS) as a software pattern combining a conventional core, direct interaction, and a built-in agent, and Intent-Level Interaction Abstraction (IIA) as the task semantics through which users inspect and control delegated work. An open transition-system model relates AIS execution to IIA states and events. Interaction contracts constrain this relation through task bindings, role-specific authority, control transitions, and outcome evidence; continuous assurance maintains scoped claims as their dependencies change. A compact disclosure contract and conditional propositions illustrate why local component validity is insufficient and how selected admission invariants can be separated from planning. Contrasting software domains expose the framework's assumptions and limits. This perspective develops a research agenda spanning application abstraction, development support, controlled execution, quality assessment, and human supervision, with the aim of making agent integration a maintainable software engineering discipline.
Related papers
- EMERGE-Policy: A Robot Mind Emerges Beyond a Single Policy [48.898967774856466]
EMERGE-Policy coordinates both capability invocation and information exchange.<n>Main Agent retains task-level state within an active context window.<n>Role-specific Sub Agents process perception, execution monitoring, verification, and memory consolidation.
arXiv Detail & Related papers (2026-08-30T16:46:06Z) - Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents [4.138547191663224]
We propose Intent-Driven Situation States (IDSS), a training-free framework that maintains an explicit situation state alongside the dialogue.<n>IDSS parses tool returns into provenance-aware entities and attributes, tracks user intents, required variables, constraints, and execution status, and propagates new facts to task constraints to update action executability.
arXiv Detail & Related papers (2026-08-16T14:14:07Z) - From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
arXiv Detail & Related papers (2026-08-03T12:52:04Z) - 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) - Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs [52.26652574704317]
Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes.<n>We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems.
arXiv Detail & Related papers (2026-05-07T14:19:17Z) - A Decoupled Human-in-the-Loop System for Controlled Autonomy in Agentic Workflows [0.0]
Human-in-the-Loop (HITL) mechanisms are typically embedded within application logic.<n>This paper presents a decoupled HITL system architecture that treats human oversight as an independent system component.
arXiv Detail & Related papers (2026-04-24T22:28:08Z) - Effective Strategies for Asynchronous Software Engineering Agents [52.76455094324273]
We introduce a structured multi-agent coordination paradigm grounded in three core SWE primitives: centralized task delegation, asynchronous execution, and isolated workspaces.<n>We find that CAID improves accuracy over single-agent baselines by 26.7% absolute on paper reproduction tasks (PaperBench) and 14.3% on Python library development tasks (Commit0).
arXiv Detail & Related papers (2026-03-23T02:26:35Z) - A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance [0.22940141855172028]
This paper presents an assurance framework for Agentic AI systems using Large Language Models (LLMs)<n>Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts.<n>The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations.
arXiv Detail & Related papers (2026-03-18T10:23:48Z) - Beyond Context Sharing: A Unified Agent Communication Protocol (ACP) for Secure, Federated, and Autonomous Agent-to-Agent (A2A) Orchestration [0.0]
This paper introduces the Agent Communication Protocol (ACP)<n>ACP provides a standardized framework for Agent-to-Agent interaction.<n>ACP reduces inter-agent communication latency by % while maintaining a zero-trust security posture.
arXiv Detail & Related papers (2026-02-11T17:02:12Z) - The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
arXiv Detail & Related papers (2026-01-21T15:22:21Z) - SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
arXiv Detail & Related papers (2025-03-11T17:53:02Z) - MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents [7.4159044558995335]
We introduce MOSS (llM-oriented Operating System Simulation), a novel framework that integrates code generation with a dynamic context management system.
At its core, the framework employs an Inversion of Control container in conjunction with decorators to enforce the least knowledge principle.
We show how this framework can enhance the efficiency and capabilities of agent development and highlight its advantages in moving towards Turing-complete agents.
arXiv Detail & Related papers (2024-09-24T14:30:21Z)
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.