arXiv AI
Jul 7
Builder, Defender, Breaker: The Case Against Removing the Human from the AI-Driven Security Lifecycle
arXiv:2607. 03215v1 Announce Type: cross Abstract: Artificial intelligence has spread across the whole of the security lifecycle.
Builder, Defender, Breaker: Measurable Independence and Bounded Autonomy When Generative Models Build, Defend and Test Software
The article discusses how generative models increasingly act as builders, defenders, and breakers of software, challenging the assumption that full autonomy is the ultimate goal. It introduces a framework that defines measurable independence between lifecycle roles based on shared generative substrates, and proposes five autonomy levels, three human roles, and five decision criteria to guide oversight. The authors argue that human authority should focus on specification, accountability, and emergency intervention, and they outline testable hypotheses and protocols to evaluate independence and oversight effectiveness.
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arXiv AI
Aug 28
Agent Mesh: Reliability Primitives for Non-Idempotent Agent Delegation - Identity Adequacy and Evidence Adequacy
The paper reports a failure study of a production agentic software‑delivery platform, analyzing 147 incidents across 81 runs. It shows that the standard reliability primitives—retry, timeout, and error‑rate circuit breaking—fail in practice, leading to costly loops, false trips, and blocked work. The authors identify two cross‑cutting causes—identity adequacy and evidence adequacy—and propose seven new reliability primitives that enforce reliability at the delegation level.
arXiv AI
Aug 28
Five Primitives for Governing Autonomous AI Agents at Runtime
The paper proposes five runtime primitives—discovery, identity, governance, attestation, and supply chain—to manage autonomous AI agents in enterprise settings. It argues that traditional control models fail because agents are transient, model-driven, and self‑discoverable, making runtime governance essential. The authors detail an implementation that mediates agent actions against policy, authorizes them via a per‑tenant vocabulary, and records them in a verifiable ledger, noting the associated operational costs and partial deployment status.
arXiv AI
Sep 11
Introducing Consort: A Spec-First Agent Framework for Enforced, Test-Driven Development on Live Database Branches
Consort is a spec‑first, test‑driven agent framework that enforces engineering discipline through immutable controls, a deterministic orchestrator, and human‑approved gates. It operates on live database branches, guiding role agents through a spec‑first design lane and a test‑driven build lane. The framework claims that enforcing tests and gates in code keeps agent‑written code honest and verifiable, while specialized roles make it maintainable.
arXiv AI
Jun 15
Regulating the Machine Contributor: Governance and Policy Alignment in Open Source
arXiv:2606. 14594v1 Announce Type: cross Abstract: AI-assisted software development has moved from line-level autocomplete to agents that can plan changes, edit files, and submit pull requests with limited human supervision.
arXiv Machine Learning
Jun 25
The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
arXiv:2606. 26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems.
arXiv AI
Jul 3
Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
arXiv:2607. 01793v1 Announce Type: new Abstract: LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks.
arXiv AI
Aug 25
AUDITA: certified auditing and causal attribution of adverse outcomes in autonomous multi-agent systems
arXiv:2608.22160v1 Announce Type: new Abstract: Physical automation is scaling toward fleets of embodied machines commanded by an AI brain. Early deployments already run factories and warehouses at p...
arXiv AI
Sep 11
AgentAudit: An Open, Extensible Framework for Full-Lifecycle Trust Evaluation of AI Agents
AgentAudit is an open, extensible framework that evaluates the full lifecycle of AI agents, assessing planning, tool selection, execution, memory, and reasoning across ten dimensions such as instruction integrity, security, and alignment. Unlike existing benchmarks that focus on single aspects, AgentAudit analyzes the entire execution trace to attribute failures to specific stages. The framework was tested on five large language models, revealing significant differences in trustworthiness even among models with similar task‑completion performance.
arXiv AI
Jul 22
Competitive and Complementary Tools
arXiv:2607. 18460v1 Announce Type: cross Abstract: Humans have always externalized thought onto tools, from the tally and the abacus to the map and, now, large language models.
Hugging Face Trending Papers
Jul 1
Risk Architecture for AI-Native Engineering Teams: An Organizational Framework for Agentic System Governance
Engineering management research has produced mature frameworks for software risk: ownership by feature, escalation by severity, and assurance by test coverage. These frameworks implicitly assume deterministic behavior, discrete and auditable change events, and clear component-to-owner mappings.
arXiv AI
Jul 10
From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
arXiv:2607. 08028v1 Announce Type: new Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context.