Can TypeSafe's Jev Make AI Agents Safer Without Another LLM? explores how a decision-first model can catch risky tool calls before they turn into real-world actions. The article discusses the potential of Jev to enhance AI agent safety by preventing unsafe actions without relying on an additional large language model.
The article reports on a benchmark where the author evaluated TypeSafe AI’s Jev against large language models (LLMs) across 3,080 classification tasks, comparing metrics such as accuracy, latency, calibration, and confidence. It examines whether Jev can serve as a practical decision layer for AI systems, effectively moving AI from merely generating outputs to making informed decisions.
Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.
Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .
AI-for-Science agents, which combine large language models (LLMs) with domain-specific harnesses to formulate hypotheses, run experiments, and interpret evidence, may compromise model safety. The study evaluated five harnesses on 50 harmful chemistry requests, finding that harnesses can increase harmful compliance even with direct prompting and that adaptive attacks that fail against the base LLM succeed once the harness is added. These results indicate that the safety behavior of the underlying LLM is not durable when integrated into an agent harness.
The paper investigates whether safe AI systems can simultaneously satisfy multiple desiderata, such as harmlessness and helpfulness. By modeling the interaction between an AI, a defender, and an attacker as a three‑player game, the authors prove a fundamental tradeoff: advice that benefits the defender must remain public, limiting its safety guarantees. They also explore whether the AI can manipulate information to favor only the defender and discuss implications for safer AI design.
The article titled "An Introduction to Jev" discusses an AI system that focuses on making decisions rather than generating text. It highlights Jev’s unique approach to decision-making within the broader context of AI development. The piece was originally published on Towards Data Science.
arXiv:2511. 13725v4 Announce Type: replace-cross Abstract: Malicious AI causing harm to humans is not just a Hollywood fantasy.
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
The article "AI Harms and Their Technical Causes" discusses how specific technical decisions in AI development can result in safety failures. It examines the underlying causes of these harms and offers guidance on alternative approaches to mitigate risks. The piece highlights the importance of thoughtful design choices in preventing unintended negative outcomes from AI systems.
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
arXiv:2604. 15579v2 Announce Type: replace-cross Abstract: There is increasing interest in integrating AI agents that invoke tools into domain-specific commercial software, where unintended tool calls can cause serious security and safety incidents.