AgentCore-8B
A model post-trained for tool use and multi-step planning.
News · 9
Amazon Bedrock AgentCore harness is now generally available: Go from idea to production-grade agent in minutes
Today, Amazon Bedrock AgentCore harness is generally available. Two API calls (CreateHarness to define an agent, and InvokeHarness to run it), and you have an agent running in seconds. The agent runs in its own isolated environment with a filesystem and shell, so it can read files, run commands, and write code safely. It remembers users and conversations across sessions, picks up skills you point it at (including the AWS-curated catalog), browses the web, calls your tools through gateway or MCP,
NVIDIA Brings Trusted, 24/7 AI Agents to Telecom Operations
Telecom operators have seen remarkable returns from using generative AI to automate network management, customer care and back-office operations. Most of that impact has been task‑based: automation that speeds up predetermined steps while people manually correlate insights and direct next steps. Automation is no longer the finish line — it’s the launchpad to autonomy. The […]
Learning to lead in a hybrid human-AI enterprise
As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across…
Agentic AI for Robot Teams
<img src="https://spectrum.ieee.org/media-library/johns-hopkins-whiting-school-of-engineering-logo-with-shield-emblem.png?id=66700256&width=980" /><br /><br /><p>This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support age
Optimising LMAPF guidance graphs using Evolutionary algorithms: Advice needed [R]
<!-- SC_OFF --><div class="md"><p>Hello,</p> <p>I'm currently working on my dissertation and feel like I could really use some advice from someone who looks at the problem with fresh eyes. I appreciate all input.</p> <p>The Problem:<br /> Multi Agent Path Finding is the problem of finding paths for several agents to their destinations. Lifelong MAPF is the same, but upon task completion an agent is assigned a new task. For my dissertation (and usually in research) agents move on a grid-like grap
AI coding agents taught robots how to install GPUs and cut zip ties
Nvidia's self-improvement program for robots enlists teams of AI coding agents.
Is it agentic enough? Benchmarking open models on your own tooling
Alibaba's model never trained as an agent — and improved agent performance across seven benchmarks
<p>Alibaba's Qwen team released Qwen-AgentWorld on Tuesday — two models trained not to act inside agent environments, but to predict what those environments return. The release covers seven domains under a single architecture: MCP, Search, Terminal, Software Engineering, Android, Web, and OS. </p><p>The release extends Alibaba's recent push into autonomous agents.<a href="https://venturebeat.com/technology/alibabas-proprietary-qwen3-7-max-can-run-for-35-hours-autonomously-and-supports-
Papers · 5
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation. Existing systems treat these as separate problems: VLM-based planners lack a unified cyber-physical action space, agent frameworks accumulate unbounded context that degrades temporal coherence, and VLA policies execute open-loop without detecting their own failures. We argue that persistent autono
A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO
We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow engine. A process harness places a policy-governed agentic layer around a deterministic workflow engine, intercepting designated control points to contribute reasoning, adaptation, and oversight while the engine retains structural authority over the process. To define the process harness rigorously, we develop the Task-Decision-Flow (TDF) model, specifying both its data schema and its execution semantics. TDF decompose
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has typically separated two uses of such experience: keeping it outside the model as natural-language rules for later prompting, or using trajectories and feedback to update the model parameters. The former is easy to interpret but can fall out of sync with the evolving policy; the latter improves the policy more broadly but provides only limited correction for local mistakes in sparse-reward settings. We present Joint Learning of Experiential Rul
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy preserving compared with commercial large models, they suffer from weak planning and limited cross website generalization. To address these limitations, we introduce the planning experience exploration and utilization (PEEU) method, which autonomously explores environments to discover experiences and utilizes hindsight experience to synthesize strictly aligned, hi
Tool use without fine-tuning
Teaching models to call tools purely through in-context demonstrations.
Repositories · 1
agent-tools
A library of well-typed tools for LLM agents.