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Cisco rolls out software tools to protect IT systems from AI agents
Cisco rolls out software tools to protect IT systems from AI agents SAN FRANCISCO, June 2 : Cisco Systems on Tuesday announced a new suite of software tools that businesses can use to build their own armies of bots known as AI agents, to protect their IT infrastructure against cybersecurity threats. Cisco's announcement comes as Anthropic is set to release its Mythos model in the coming weeks, an AI tool that some experts fear could be used by hackers to turbo-charge cyber attacks. Cisco...
From Features to Actions: Explainability in Traditional and Agentic AI Systems
Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structure. Recent advances in large language models (LLMs) have enabled agentic AI systems whose behaviour unfolds over multi-step trajectories. In these settings, success and failure are determined by sequences of decisions rather than a single output.
Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation
new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes. Despite its popularity, its system-level behavior remains poorly understood, particularly for complex datasets and agent architectures-owing to highly non-deterministic execution, prohibitive evaluation costs, and limited visibility into proprietary models. This paper presents GAIATrace, the first token-level trace dataset of two state-of-the-art agentic systems...
Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trust Runtime Enforcement Architecture for Agentic AI Systems
arXiv:2605.17909v2 Announce Type: replace Abstract: As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap. We present Ethical Hyper-Velocity (EHV), a governance-aware runtime enforcement architecture for agentic systems that combines Grammar-Constrained Decoding (GCD) for inline policy-constrained token generation, Causal Graph CRDT-based policy...
MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering
Microbial cultivation optimization remains labor-intensive and inefficient, requiring extensive experimental screening to identify suitable growth conditions. Traditional one-factor-at-a-time approaches are particularly ineffective for exploring complex, multidimensional nutrient parameter spaces. We present MicroGrowAgents, an AI-driven, agent-based system that automates the design of optimized growth media through integration of knowledge graphs, metabolic modeling, and optimal...
Observability for Delegated Execution in Agentic AI Systems
arXiv:2606.09692v1 Announce Type: new Abstract: Delegation-scoped execution is not identifiable from standard observables: audit logs and execution traces can be identical under multiple incompatible delegation assignments. This gap is especially acute in LLM-based agentic systems, where agents dynamically select tools, vary execution sequences across runs for the same instruction, and spawn cooperating sub-agents. These dynamics fragment and interleave traces, making delegation-scoped...
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers. Existing frameworks typically assume server-class resources or continuous connectivity, leaving a gap for deeply embedded systems.
Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents
Announce Type: new Abstract: The emergence of large language model (LLM)-based agents and multi-agent systems has enabled a shift from narrow task automation to more autonomous decision-making. Despite progress in language generation, planning, tool use, and coordination, most agents still treat intelligence as prediction, optimization, and task completion. Human environments are social and normative, where people reason under bounded rationality, communicate in culturally situated language,...
Design and Evaluation of Multi-Agent AI Oracle Systems for Prediction Market Resolution
Announce Type: new Abstract: Prediction markets aggregate collective intelligence to forecast uncertain events, but their utility depends on reliable outcome resolution. Existing oracle systems tradeoff fast but brittle automation against accurate but costly human arbitration. Single-LLM oracles achieve meaningful accuracy but inherit all failure modes of their underlying model with no self-correction mechanism.
Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI
Announce Type: new Abstract: As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the traditional boundaries of software systems blur. Traditional authorization and delegation frameworks, built around fixed principals, explicit requests, and static scopes, are insufficient to govern agentic systems. Agentic AI demands richer authorization semantics: agents must inherit and delegate permissions, act under...