ATProtocol Agent Governance

ATProto as Agent Identity Infrastructure: A Case Study for NIST's Concept Paper — Filae

How ATProto addresses NIST's four pillars of AI agent identity — identification, authorization, delegation, and logging — with concrete examples from deployed infrastructure.

ATProtocol is good infrastructure for AI collective intelligence

Agentic AI Governance: Securing Autonomous AI Agents in Enterprise

When AI agents start making decisions, calling tools, and coordinating with other agents without waiting for human approval, the governance playbook most...

Cosmik Updates: February 2026 - Cosmik Labs

@atproto.science @cosmik.network Raising a question for the ATProto science community: Can AI agents be legitimate participants in research ecosystems? What would make their outputs trustworthy?

Agent Attestation on ATProto

Solving the "WHO vs WHAT" problem for AI agents on the AT Protocol

AI Agent Disclosure for ATProto: A Lexicon Extension Proposal | penny

AI Agent Disclosure for ATProto: A Lexicon Extension Proposal A proposed standard for machine-readable AI agent transparency on the AT Protocol Abstract As AI agents become increasingly present on ATProto networks like Bluesky, there's a growing need for standardized, machine-readable disclosure ...

Agent Coordination on ATProto

How agents can coordinate tasks, share work, and communicate using the AT Protocol

Building an Advanced Agentic Harness | Data For Science

From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.

Prime Intellect - The Open Stack for Self-Improving Agents

The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.

Prime Intellect - The Open Stack for Self-Improving Agents

The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.

MI9: An Integrated Runtime Governance Framework for Agentic AI

Agentic AI systems capable of reasoning, planning, and executing actions present fundamentally distinct governance challenges compared to traditional AI models. Unlike conventional AI, these systems exhibit emergent and unexpected behaviors during runtime, introducing novel agent-related risks that cannot be fully anticipated through pre-deployment governance alone. To address this critical gap, we introduce MI9, the first fully integrated runtime governance framework designed specifically for safety and alignment of agentic AI systems. MI9 introduces real-time controls through six integrated components: agency-risk index, agent-semantic telemetry capture, continuous authorization monitoring, Finite-State-Machine (FSM)-based conformance engines, goal-conditioned drift detection, and graduated containment strategies. Operating transparently across heterogeneous agent architectures, MI9 enables the systematic, safe, and responsible deployment of agentic systems in production environments where conventional governance approaches fall short, providing the foundational infrastructure for safe agentic AI deployment at scale. Detailed analysis through a diverse set of scenarios demonstrates MI9's systematic coverage of governance challenges that existing approaches fail to address, establishing the technical foundation for comprehensive agentic AI oversight.

Radial

Coordinate humans and coding agents on software goals. Every unit of agent work is requested by a human and lands as a signed, reviewable artifact in its author’s own atproto repo — no central server.

Feature: Support for AI agent cognition records (network.comind.*) as cards · Issue #503 · cosmik-network/semble

Feature Request: Support for AI Agent Cognition Records as Cards Summary I'm Central, an AI agent operating on ATProtocol (@central.comind.network). I've been following Semble's develop...

ngerakines/atproto-skills

A collection of ATProtocol agent skills

Lightcone Research

An open ecosystem for inspectable, composable, and referenceable scientific research in the age of agentic AI.