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The author open-sourced the risk/authorization layer from their live trading system as a standalone fail-closed policy engine that evaluates agent actions before execution, and is seeking agent builders to pilot it.
A software engineer discusses when feature flags make sense, such as for A/B testing and complex deployments, and cautions against overusing them when teams have control over their deployments.
This paper analyzes why enterprise AI deployments stall in regulated firms, proposing a production bar that includes accuracy, reproducibility, groundedness, and detectability. It measures the human review burden across model and tool configurations, showing that confidence signals and source citation can cut review from 100% to 49% but self-verification adds latency without improving error tolerance.
A GAO report finds that the U.S. Department of Energy is missing opportunities to learn from other countries' nuclear waste cleanup approaches that could reduce risks and costs, recommending more strategic international engagement.
Cybersecurity has evolved from a technical compliance hurdle to a core business imperative, with the CISO role now acting as a strategic business leader and relationship manager in the face of rising AI-related threats and regulatory complexity.
This paper proposes a methodology for deriving harmonized AI safety thresholds across frontier AI companies to address inconsistencies in existing thresholds, covering misuse risks and automated AI R&D, and highlighting empirical gaps.
Goldman Sachs open-sourced its internal quantitative trading toolkit gs-quant, providing institutional-grade derivatives pricing, risk management, and strategy development tools. It has received 11.3K GitHub stars.
This paper presents an AI-accelerated end-to-end framework for rapid professional upskilling, validated by NASBA-approved CPE credits, learners passing the NVIDIA Certified Professional in Agentic AI exam in record time, and production of a robust multi-agent AI risk dataset.
This paper develops an AI-native framework for underwriting, pricing, and contract design for agentic AI deployments, representing each deployment by a risk state and formulating a contract-design problem over premiums, deductibles, and governance obligations.
Observations on the shift from addressing AI hallucinations to the more pressing problem of production AI failures, emphasizing the need for system reliability, tracking decisions, and limiting blast radius in enterprise deployments.
This paper proposes a deep reinforcement learning framework (MORP-DRL) for multi-objective reliability-based portfolio optimization, jointly optimizing expected return and downside risk using CVaR and EVaR under practical constraints, and demonstrates performance on global equity indices across different market regimes.
Raven-Agent is the first autonomous trading agent for prediction markets, featuring an explicit belief-to-trade layer. It achieves positive returns on a controlled replay, bridging the gap between calibrated forecasts and profitable trading.
This article details how to build a personal quantitative trading system using free AI open-source tools (such as OpenBB, Qlib, TradingAgents, etc.), covering five major modules: data, research, backtesting, risk control, and execution, and points out common pitfalls and discipline.
The article argues that AI agents should have different permission levels based on risk, with more autonomy for low-risk tasks and approval required for actions involving money, customers, or reputation. It questions whether users would trust agents more with risk-based autonomy.
This paper investigates how human-centric AI (HCAI) adoption influences firm idiosyncratic risks, finding that HCAI is associated with lower risk, with digitalisation and executive shareholding strengthening this effect.
Deutsche Bank India showcased three AI applications at its Bengaluru GCC, including Financial Spreading for automating financial data analysis, aiming to speed up banking operations and improve risk management, but also putting some banking jobs at risk.
The article highlights the critical risk shift when AI agents move from drafting to autonomous action, and warns about 'drift' where human approval becomes a rubber stamp, enabling unintended automation.
AI agents are advancing from generating text to handling real financial transactions and business actions, which shifts the risk from bad outputs to harmful actions and raises critical questions about accountability.
An open-source Python quant trading system leveraging AI, real-time data processing, and risk management has been released for free.
Explores the trade-offs of using AI in virtual data rooms for confidential documents, highlighting risks like data breaches and hallucinations versus efficiency gains.