Agents & autonomy

Large language model agents often spend substantial wall-clock time waiting for tool call results. Tool-call speculation can hide this latency by predicting and pre-executing an agent's next tool call if the prediction matches the agent's eventual tool call, but existing speculators are typically separate draft models or cached traces that are poorly aligned with the deployed agent's own behavior. We identify this speculator-agent gap and show that the target agent itself is a strong next-call s

Am Bodensee wird ein Roboter getestet, der für den Einsatz im Obstanbau entwickelt wurde. Er soll künftig bei der Apfelernte eingesetzt werden.

CORAS is a FedRAMP High-certified agentic-AI “decision maker” that has already been authorized for use at the Department of Defense. The post GSA inks agentic AI OneGov deal with CORAS appeared first on FedScoop .

This paper presents a cooperative indoor UAV guidance framework that combines a shared voxel-map world model with a multi-agent Soft Actor-Critic (MASAC) controller. Multiple drones fuse 360 LiDAR observations into a common world-frame occupancy map, which is converted into a compact bird's-eye-view (BEV) representation and provided to each agent as an ego-aligned local crop. This integrate-in-world, act-in- ego design enables consistent multi-UAV spatial fusion whilst retaining decentralised co

Large language model (LLM) agents increasingly rely on invoking external tools to complete real-world tasks. Tool retrieval, which selects a small task-relevant subset from a library of thousands of tools before the agent acts, has therefore become a critical component of LLM agent pipelines. However, existing retrievers either score each tool in isolation or assemble the tool set sequentially, so the joint utility of a candidate set is never evaluated as a whole. In this paper, we propose HYSET

Professionals are burning through tokens, but smart business leaders are finding ways to balance costs and value creation.

Microsoft a présenté le 27 juillet 2026 une architecture de sécurité entièrement bâtie sur l'IA agentique, avec une préversion publique annoncée pour le 3 août 2026.

Workflow automation company Tines Ltd. today launched Tines 3B, an artificial intelligence-native platform for building, running and governing enterprise workflows, applications and agents. The product targets a problem Tines has taken to calling “Wild Code,” the sprawl of AI-generated software now being assembled inside enterprises by employees working outside traditional information technology processes. Much of […] The post Tines seeks to tame ‘wild code’ AI sprawl appeared first on SiliconAN

Snowflake Inc. today introduced Cortex AI Gateway, a centralized control layer that lets enterprises connect, govern and monitor artificial intelligence agents as they reach into models, tools, Model Context Protocol servers and internal systems. The company cast the gateway as the connective tissue for the “agentic enterprise,” the point at which autonomous software agents start […] The post Snowflake debuts Cortex AI Gateway to govern and monitor enterprise AI agents appeared first on SiliconA

Secure artificial intelligence infrastructure startup Dymium Inc. today introduced GhostAI, a gateway designed to apply security and governance policies across the models, data, context and tools used in enterprise AI systems. The company said GhostAI sits between enterprise data and the AI models, agents and tools seeking to access it. The gateway inspects each interaction, […] The post Exclusive: Dymium introduces single gateway to govern enterprise AI use appeared first on SiliconANGLE .

Agent infrastructure startup Diagrid Inc. today released Catalyst 2.0, an update to its managed workflow engine that adds automatic failure recovery and cryptographic verification to artificial intelligence agents built on frameworks such a LangGraph, Microsoft Agent Framework and Google’s Agent Development Kit. Developers do not have to rebuild anything to use it. Teams add a […] The post Diagrid Catalyst 2.0 adds durable execution to more than 10 agent frameworks appeared first on SiliconANGLE

AI agents can impress in a demo and still fumble in production. Diagrid’s Catalyst 2.0 aims to make them more The post Diagrid gives failed AI agents a way to resume appeared first on The New Stack .

Rimini Street, Inc. (Nasdaq: RMNI), the Software Support and Agentic AI ERP Company™ and the leading third-party support provider for Oracle, SAP and VMware software, today announced that AUTOBACS SEVEN Co., Ltd. celebrates its 10-year partnership with Rimini Street, marking a decade of stable core operations and reinvestment in innovation.

Perplexity has expanded its agentic Personal Computer tool to Windows, allowing computers running the world's most popular OS to be used as a locally run AI system. Like the Mac version that Perplexity launched in April, Personal Computer for Windows operates like a "general-purpose digital worker" that can access local files and apps to perform […]

Die Nutzung von KI-Agenten direkt auf dem Rechner kann Gefahren mit sich bringen. Das zeigt eine soeben entdecktes Sicherheitsloch in Claude Cowork für den Mac.

Trulioo, a global risk intelligence platform, today announced the UBO Discovery Agent, the newest layer in Trulioo’s UBO Discovery capability inside its business risk and Know Your Business (KYB) verification workflow.

Wie ich eine KI dazu bringe, guten Code zu schreiben - ganz praktisch. Ein Erfahrungsbericht von Felix Knorr ( KI , Softwareentwicklung )

Like genies of folklore, AI agents take their instructions literally – to potentially disastrous effect. We must track their ability to do what we actually mean In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers. Whoever was behind it captured internal security credentials and moved through systems over a weekend, running thousands of actions from a swarm of tempora

A wheeled bot that monitors air quality is joining robotic vacuums and scrubbers in Terminal B.

Inside the slick new Terminal B at New York’s LaGuardia Airport, a headless robotic “dog” is beginning to patrol the floors. As bemused airport travelers watch from close by, the four-wheeled device rolls around baggage claim, where it’s been deployed for a demonstration. Its job: sniffing. Well, sort of. The robot is armed with air quality detectors that the airport team says help monitor the terminal for pollutants.  This robot, along with two others, constitutes a fledgling automated fle

A wheeled bot that monitors air quality is joining robotic vacuums and scrubbers in Terminal B.

Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf. Primary care guards against diagnostic errors and unsafe care; agents assisting in this domain warrant evaluation against the same risks. Current benchmarks focus on medical knowledge, assessed through isolated question-answering or clinician-facing tasks. PatientAgentBench benchmarks patient-facing agentic healthcare; it evaluates a foundation mode

Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol). IMACS (Intelligent Multi-Agent Collaboration System) separates the three into orthogonal, independently swappable layers. Classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) becomes executable, validated configurati

NewMod law firm Crosby is to provide ‘professional liability insurance for [their] agents, so that they can do autonomous legal work’, in what is an ...

Supply chain decision-making is rapidly transforming with the rise of agentic AI - highly autonomous systems that can operate on complex, long-horizon tasks. Yet the adoption of agentic systems outpaces their governance: existing taxonomies of autonomy only offer discrete classifications, rely on subjective judgement, and cannot track autonomy across a system's life cycle, leaving enterprises unable to assess the risks of increasingly autonomous supply chain agents. This paper proposes the Agent

Large language model (LLM) agents are increasingly entrusted with natural-language workflow instructions (e.g., retail-payment policies) that specify not only what outcome to achieve, but also which steps, branches, and tool interactions are permitted. When these instructions are supplied as prompt context, however, the model retains control over both procedure selection and step execution. As interactions accumulate, an agent can skip required steps, take unsupported branches, or execute a vali

Language-model agents are increasingly deployed under standing instructions: a system prompt, a policy file, or a skills document is placed in context, and the agent is trusted to let it govern every action that follows. Existing benchmarks rarely test this deployment pattern directly; they measure whether an agent can complete a task, not whether a long, binding policy document actually constrains its behavior over an extended tool-use horizon. We present HANDBOOK.md, a benchmark of 65 agentic

Tool-using agents expose structured calls but commonly attach free-form rationales. Such rationales are neither authorization nor reliable introspection. We present Explanation-Bound Tool Execution (EBTE), a claim-carrying mediation layer that converts decision-relevant rationale content into typed action claims and checks them against server-held intent, policy, payload, tool, risk, provenance, and freshness facts. EBTE cannot widen baseline authority: conflicts deny, incomplete or uncertain cl

AWS released a public preview of the GuardDuty investigation agent, which correlates findings, 90-day activity logs, and resource topologies into structured reports with risk ratings, confidence scores, and MITRE ATT&CK classification. It is reachable through the AWS MCP Server, so investigations can run from agentic tooling. Preview quotas cap usage at 10 investigations per account per day. By Steef-Jan Wiggers

This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes three contributions. First, it introduces a structured contract as the semantic-alignment and task-execution artifact that translates natural language requirements into explicit interfaces, constraints, validation checks, and rollback rules. Second, it incorporates hardware information into the feedback loop by feeding HLS, Vivado, PYNQ runtime, po

arXiv:2607.22607v1 Announce Type: new Abstract: The prospective clinical evaluation of artificial intelligence in medicine has expanded rapidly, but the global AI clinical trial landscape remains incompletely characterized. We systematically identified AI-related trials registered in ClinicalTrials.gov using a broad keyword search followed by an LLM-based classifier. Each trial was classified across seven dimensions: clinical function, data modality, specialty, AI integration and autonomy, workf

arXiv:2607.23207v1 Announce Type: new Abstract: The emerging infrastructure for AI-agent identity has converged, in industry practice and research proposals alike, on a single resolution of the tension between accountability and privacy: make every agent identifiable. We document a national system in China -- built as national infrastructure and scheduled for public launch in Q3 2026 -- that occupies a different and underexplored point in the same design space: an agent is associated with a veri

arXiv:2607.23336v1 Announce Type: new Abstract: The built environment is on the cusp of populating itself with autonomous artificial agents. AI systems that advise, control and coordinate are being deployed across retrofit, operation and mobility faster than their collective behaviour is studied. The dominant framing treats each agent as a tool operating on a passive building, governance reduced to single-agent safety, which is inadequate. A building, a street, or a city is more accurately model

arXiv:2607.23539v1 Announce Type: new Abstract: AI agents can transact online on behalf of a human principal---browsing, paying, receiving, and reviewing---without linking a transaction to a principal. That architecture starves algorithmic discrimination of its inputs---identity, purchase history, location history, behavioral traces, and demographic proxies---but also forecloses its proof. Disparate-treatment needs comparators; disparate-impact needs protected-class baselines; and Iqbal-era plea

arXiv:2607.23931v1 Announce Type: new Abstract: The aggregation benefit of a committee of artificial intelligence (AI) agents comes from complementary information across members. Classical voting guarantees assume independent errors. Language-model errors often co-occur on the same cases. We combine Sah-Stiglitz screening with error dependence that can differ between good and bad cases. In a homogeneous exchangeable Gaussian-copula model, shared errors create a positive asymptotic error floor fo

arXiv:2607.22953v1 Announce Type: cross Abstract: Modern AI systems bring societal risks such as mass surveillance, extreme concentrations of power, and loss of user autonomy---calling into question a model where third-parties collect and control massive amounts of user data. Users require a sovereign system to securely own, govern, and disclose their context while remaining compliant across regulated domains with strict provenance, interpretability, and policy adherence. Perspective-aware AI ap

arXiv:2607.23438v1 Announce Type: cross Abstract: As AI systems increasingly exhibit agentic behavior, discussions of autonomy often conflate what systems are technically capable of doing with what they should be permitted to do in practice. This paper introduces a governance framework that explicitly separates Allowed Autonomy Levels (AAL), which define the degree of autonomy an AI agent is authorized to exercise given risk, oversight, and accountability considerations, from Autonomous Capabili

arXiv:2607.23941v1 Announce Type: cross Abstract: Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform Reddit based on temporal, community, linguistic, and semantic features. Clustering analysis reveals 18 distinct bot types spanning content-specialized,

arXiv:2603.28371v2 Announce Type: replace Abstract: When an agent can articulate why something works, we typically take this as evidence of genuine understanding. This presupposes that effective action and correct explanation covary, and that coherent explanation reliably signals both. I argue that this assumption fails for contemporary Large Language Models (LLMs). I introduce what I call the Bidirectional Coherence Paradox: competence and grounding not only dissociate but invert across epistem