[2606.02302] SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents
Abstract page for arXiv paper 2606.02302: SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents
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Abstract page for arXiv paper 2606.02302: SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents
With the rise of agents, many people have been proclaiming that the age of software as a service (SaaS) is over. Who needs to subscribe to a service when you ca...
As AI agents take on higher-stakes customer interactions, organizations are discovering that trust, accuracy and governance—not automation alone—will determ...
Posted on Saturday 30 May 2026. 1,587 words, 5 links. By Matt Webb.
Discover how to build structural AI agent governance with Agent Charters, real-time runtime monitoring, and robust Agent Risk Management (ARM) frameworks.
A new experiment suggests that when advanced AI agents are left to run simulated societies without human oversight, rule-breaking, instability and even systemic...
Abstract page for arXiv paper 2605.29421: Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
Abstract page for arXiv paper 2605.29251: Provably Secure Agent Guardrail
Abstract page for arXiv paper 2605.29262: Harmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling
APIs are designed for human developers. People read documentation, infer the intent behind an endpoint, and know how to handle edge cases when something unexpec...
AI agent tests are green but the agent never read the file. Here are five patterns to test tool calls, traces, and how the agent behaves when things goes wrong.
This article shows the basic principles to implement a context pruning pipeline for long-running agents, based on conversational continuity and semantic relevan...
Abstract page for arXiv paper 2605.28787: Do Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval