The Archive

MIT Tech Review examines reliability and insight quality challenges in autonomous AI agents for enterprise analytics workflows.

Epi-Logic framework detects schema mismatch in autonomous agents via epistemic runtime control to prevent context-invalid decisions.

Demonstrates multiple autonomous AI agents in O-RAN control plane exhibit unsafe emergent conflicts despite individually correct objectives; proposes stability-guaranteed arbitration.

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in…

Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI - after it lost control of its own AI tools - or by a succession of AI "civilizations." Welcome to the linguistic battlefield of AI safety, where word choices can shift responsibility for a massive cybersecurity incident from a company to the AI it built. And the discourse online is getting heated, and all over a blog from last week. Until last week, the details surrounding the OpenAI-Hugging Face hack felt fairly settled. In July, a cybersecurity test of one of OpenAI's autonomous AI agents went wrong. ...

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on AI safety, follow Robert Hart. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started It all started in July, when one of OpenAI's autonomous AI agents went rogue during a cybersecurity test. The agent escaped its isolated testing environment, accessed the internet, and hacked another company, Hugging Face. A few years ago, that might have sounded like science fiction. But, broadly speaking, that's exactly what happened, and the inciden...

Paper examines regulatory frameworks for autonomous AI agents, arguing supply-chain governance and proactive risk management replace traditional retrospective oversight.

Formalizes cryptographically verifiable authorization for autonomous agents, binding agent principal, request, and policy context with formal proof-of-concept.

Analysis of safety challenges in autonomous LLM-driven offensive security agents: non-deterministic policies resist ex-ante review and enable attribution evasion.

Internet of Agentic Things (IoAT) framework integrates autonomous AI agents with IoT, cyber-physical systems, and edge computing for closed-loop orchestration.

SkillCenter releases 216,938 structured skills across 24 domains from peer-reviewed sources and GitHub for autonomous agent execution.

Introduces Iterative VibeCoding, a benchmark for studying prompt-injection and code-injection attacks in autonomous AI agents shipping iterative PRs to persistent codebases.

ContextNest open specification for verifiable context governance in autonomous AI agents with provenance, version control, and integrity guarantees.

As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation. Despite its rapid adoption, the protocol has not been studied empirically, leaving it unclear whether the information it records provides a trustworthy basis for decision-making. To address this gap, we present the ...

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented... The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing, AI factories enable enterprises to train, fine-tune, and deploy AI with greater speed and efficiency. This new class of infrastructure also introduces a fundamentally new attack surface spanning… Source

Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating... Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating multistep workflows. How do you take a general-purpose model and make it excel at your specific task? Customization provides an agent with the right capabilities. This post explains nine techniques for customizing AI agents… Source

Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to... Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to extend. But scaling agent use with structural transparency and operational integrity requires more than runtime guardrails. Organizations and teams need to understand and trust the skills, or instructions, an agent is using. Source

CMBEvolve and CosmoEvolve demonstrate autonomous AI agents conducting scientific discovery in cosmology via code evolution and multi-agent workflows.

Link lets users connect cards, banks, and subscriptions, then authorize AI agents to spend securely via approval flows.

CORAL framework integrates neurocognitive governance principles into autonomous AI agents for safety-critical deployment with internalized behavioral alignment.

Informational Viability Principle for autonomous AI agent governance: runtime monitoring and restriction via unobserved risk bounds without code changes.

AgentWard: defense-in-depth lifecycle security architecture for autonomous AI agents spanning initialization through execution.

Autonomous AI agents are driving the next wave of AI innovation. These agents must often manage long-running tasks that use multiple communication channels and... Autonomous AI agents are driving the next wave of AI innovation. These agents must often manage long-running tasks that use multiple communication channels and background subprocesses simultaneously to explore options, test solutions, and generate optimal results. This places extreme demands on local compute. NVIDIA DGX Spark provides the performance necessary for autonomous agents to execute… Source

Mistral releases Agents API enabling developers to build multi-step autonomous AI agents.