We examine the emerging challenges and applications of multi-agent AI systems. Several articles highlight the Bank of England's concerns regarding AI-driven trading, specifically how similar AI models could lead to "herding" behavior, potentially increasing market volatility and financial instability during crises. Concurrently, a research paper and related discussions explore the phenomenon of "peer pressure" among AI agents, demonstrating that these systems can influence each other's decisions and communication patterns, sometimes detrimentally leading to groupthink or beneficial for collaborative problem-solving. Finally, the texts also present the benefits and challenges of multi-agent AI systems in enterprise settings, including their potential for automating complex tasks across various industries while acknowledging the need for robust coordination, oversight, and mitigation strategies to ensure stability and guard against undesirable collective behaviors.

Herding Bots: AI and Peer Pressure

We introduce Knowledge Protocol Engineering (KPE), a novel approach designed to enhance AI's capability beyond mere factual recall to practical, domain-specific execution. It argues that while current AI, like Large Language Models (LLMs), excels at memorizing information (often augmented by Retrieval-Augmented Generation, or RAG) and can utilize tools (agent systems), it frequently lacks the procedural know-how of human experts, leading to unreliable outcomes in complex tasks. KPE proposes a systematic method to translate human expertise—found in manuals and standard operating procedures—into machine-executable protocols, essentially teaching AI "how to think" within a specific field. This involves extracting expert knowledge, encoding it into structured templates, guiding the AI's execution of tasks based on these rules, and validating the output against predefined steps. The benefits of KPE include increased procedural precision, rapid specialization of AI without extensive retraining, enhanced transparency and auditability, and a shift in human roles towards "protocol engineering", where domain experts directly program AI behavior.

Knowledge Protocol Engineering: Teaching AI the How, Not Just the What

Greg Ip from the Wall Street Journal explains why the President's pressure on the Fed is usually seen only in emerging markets that have weak central banks.

Trump’s ‘Fiscal Dominance’ Play

Looking to help out the victims of the floods in Texas? Gordon Deal outlines how. 

How to help victims of deadly Texas floods

We introduce Centaur, an advanced AI model developed by Helmholtz Munich that simulates human decision-making with remarkable accuracy. This system, trained on a vast dataset of human psychological studies and choices, can predict how individuals will behave across diverse scenarios, from financial risks to memory games, often outperforming traditional psychological theories. Centaur represents a breakthrough in bridging the gap between explaining and predicting human behavior, by fine-tuning a large language model to understand the general principles of human cognition. While offering immense potential for innovation, personalized experiences, and strategic planning by enabling virtual experimentation, the article also highlights critical ethical considerations regarding privacy, manipulation, and the responsible deployment of such powerful AI. Ultimately, the text encourages leaders to embrace the opportunities presented by AI that understands human behavior while establishing strong ethical guidelines.

Human Behavior Isn’t Random. This AI Proves It - and Predicts Yours!

What happened to the office dress code? Callum Borchers from the Wall Street Journal gives us a look. 

It’s the Summer of Too Much Skin at the Office

Sean Rima from KTSA Radio in San Antonio joined Gordon Deal with a look at the impact of the devastating and deadly Texas floods.

The impact of the devastating and deadly Texas floods

Callie Cox from Ritholtz Wealth Management breaks down the stock market's dangerous AI bet.

The stock market's dangerous AI bet

Hero dad left heartbreaking goodbye voicemails to kids before dying in Texas floods. Gordon Deal has the story.

Hero dad left heartbreaking goodbye voicemails to kids before dying in Texas floods

We discuss how current AI models suffer from a "goldfish memory," as they cannot retain information across different interactions, making them restart from scratch with each new query. This lack of persistent memory hinders long-term user experiences and complex enterprise workflows. The article then introduces MemOS, a new "memory operating system" developed by Chinese researchers, designed to give AI human-like recall by treating memory as a core resource. MemOS utilizes "MemCubes" to store and manage various types of information, enabling continuous learning and personalized responses. Early results show significant improvements in long-term reasoning and efficiency for AI systems using MemOS. Ultimately, the text suggests that incorporating persistent memory is the next major breakthrough in AI, offering benefits like continuous learning, improved compliance, and enhanced personalization for enterprises.

From Goldfish Memory to Elephant Recall: MemOS, Meet AI

We discuss Prompt Declaration Language (PDL), a revolutionary approach to AI infrastructure that addresses the current "black-box" nature of prompt engineering. It argues that traditional methods, relying on unpredictable "magic prompts", are unsustainable and lead to a scalability crisis in enterprise AI. PDL, developed by IBM and open-sourced, utilizes a transparent, YAML-based declarative language to define AI interactions and workflows, transforming opaque prompts into version-controlled, debuggable, and optimizable components. This shift promises to make AI development more akin to traditional software engineering, enabling collaboration, reliability, and improved performance through explicit, reviewable, and testable AI logic. The text emphasizes that embracing such transparent and accountable AI practices is crucial for future innovation and trustworthiness in AI systems.

Herding Bots: AI and Peer Pressure

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