This paper introduces CAGE-1, an evaluation framework for deciding the readiness of enterprise agents for deployment, focusing on control, assurance, and governance.

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

SessionBound is a system that creates short-lived, budgeted, and auditable database sessions for AI agents based on approved enterprise tasks.

This paper introduces AGL-1, a vendor-neutral reference model for governing enterprise AI, addressing challenges such as authorization, contextual lineage, and agentic execution.

The paper introduces Beyond Zero, a new security paradigm that moves beyond traditional zero trust by performing per-resource and method access decisions at machine speed to secure the enterprise agai…

The paper introduces ADR, a novel, production-proven detection system that provides high-fidelity security monitoring for AI agents operating via the Model Context Protocol, significantly outperformin…

The paper introduces AgentCyberRange, an open, multi-range infrastructure for measuring autonomous cyber attack capability in realistic cyber ranges, and evaluates six frontier AI systems.

The paper introduces EnterpriseClawBench, an enterprise agent benchmark with 852 tasks and evaluation protocol, achieving a best configuration score of 0.663.

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The ORBIT project developed an agentic AI system, ORBIT, to address operational pain points in the Network Operations Center (NOC) workflow by integrating it into ServiceNow and providing routine auto…

The paper proposes Multi-Agent Computer Use (MACU) systems, which significantly improve performance on complex, long-horizon tasks by enabling parallel execution and dynamic task decomposition compare…

PocketAgents introduces a manifest-driven framework for autonomous defense agents, enabling measurable and attributable LLM-driven security responses by strictly controlling agent actions and telemetr…

The paper introduces a data-centric optimization pipeline to improve coding agents' ability to interact with a branching lakehouse, showing significant accuracy gains by treating agent evaluation as a…

This tutorial explores advances and challenges in deploying large language model-based agentic systems across industries, with a focus on reasoning and planning, multi-agent coordination, and evaluati…

The paper introduces AgentWard, a lifecycle-oriented, defense-in-depth architecture designed to systematically secure autonomous AI agents by protecting them across all stages of their operation.

This paper introduces progressive crystallization, a lifecycle for AI agents in IT operations that converts validated agent behaviors into cheaper and more reproducible deterministic workflows, increa…

AgentWall is a runtime safety layer that intercepts and evaluates all proposed actions from local AI agents against a declarative policy, ensuring safety before execution.

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex open-world agent deployments.

The paper introduces AgentDoG 1.5, a lightweight and scalable alignment framework that significantly improves AI agent safety and security for complex, open-world agentic scenarios.