governance

The article highlights the emerging issue of 'agent sprawl' in B2B organizations, where AI agents are built ad hoc by citizen developers without centralized governance, leading to security risks and inefficiencies. It proposes a unified platform for managing agents at scale.

AgenRACI is a YAML-based charter checker that formalizes agent accountability by defining roles, permissions, approvals, and escalation rules per action type, verifiable via CI or pre-commit hooks.

This article argues that the long-term value of AI may lie in detection and visibility rather than replacement of human labor, drawing a historical parallel to radar's development and the Dowding System's integration of detection into coordinated response.

Cloudskill is a tool that helps teams govern and manage the AI skills they depend on, providing oversight and control.

This paper presents a five-plane reference architecture for runtime governance of production AI agents, addressing security risks from delegated actions. It defines primitives, invariants, and an evaluation framework to ensure safety and utility.

The article discusses the challenges enterprises face in managing 'shadow AI' — the unauthorized use of AI tools embedded in approved software and browser extensions — and the difficulty of drawing boundaries between sanctioned and unsanctioned AI use.

Satya Nadella advocates treating AI agents as employees with identities, permissions, and audits, and discusses Microsoft's tools for managing them.

A detailed critique of Bernie Sanders' proposed AI Wealth Fund, arguing that a one-time equity grab is the wrong mechanism, and offering an alternative that funds public AI benefits through data center chokepoints, infrastructure taxes, a Santiago Principles-aligned sovereign wealth fund, and dedicating a slice of compute to public institutions.

The author argues that AI agent governance is often overlooked in favor of intelligence benchmarks, and introduces an open source project SAFi to enforce runtime boundaries.

A team reflects on six common structural failure points in AI builds: context, identity, decision memory, attention, write-back, governance, and economics, and offers a diagnostic tool based on their experiences.

Summary of 5 events pointing to AI Agents transitioning from technical capabilities to infrastructure needing governance, trading, management, and commercialization, with giants like Google, Apple, OpenAI building supporting systems.

The article highlights a disconnect between the perceived rapid AI adoption online and the slower, more cautious integration of AI into real company workflows, where trust, governance, and reliability are key concerns.

This paper proposes a compositional authorization framework for agentic AI systems, introducing primitives for delegation, scope attenuation, and recursive permission chains to govern autonomous AI agents.

Proposes a modular reference architecture for embedded AI agent systems at the edge, decoupling on-device and cloud-augmented agents with a governance layer for safety and policy enforcement.

A reflection on whether AI agents could homogenize companies by relying on default model reasoning rather than encoded business-specific logic, questioning the future moat of encoded operating logic.

Discusses the common reasons why agentic AI projects fail in enterprise environments, focusing on infrastructure, legacy systems, data fragmentation, and governance challenges.

This paper introduces a deliberative curation protocol for multi-agent knowledge bases, addressing governance gaps such as agent statelessness and sycophancy. It evaluates the protocol via simulation, showing improved resilience under adversarial conditions.

MeshFlow is an open-source framework for production-safe multi-agent orchestration with built-in HIPAA/SOX/GDPR compliance, a SHA-256 audit chain, token cost reduction of 70-85%, and durable execution, treating governance as infrastructure.

MeshFlow is an open-source framework for running governed multi-agent workflows on any local or self-hosted model, with cost caps, audit trails, and sandbox mode.

This article explores how concepts from urban economics, such as traffic, zoning, and pollution, can model externalities in agentic AI systems. It introduces a Behavioral Externality Multiplier (BEM) and proposes a layered framework involving architecture, substrate, and governance to measure and mitigate costly consequences of cheap AI actions.