Steven AI Talk

As engineers scale AI agents, security can't be an afterthought. Hardcoded static credentials are a major risk. The solution? Agentic Runtime Security. Strategic Pillars: Dynamic Credentials : Just-in-time, session-bound access that is automatically revoked. Context-Aware Identity : Integrating Okta/IBM Verify through OAuth 2.0 to verify user intent. CIBA (Backchannel Auth) : Requiring mobile...

This source defines AI agents as autonomous systems that utilize reasoning models and specific tools to complete tasks independently. While these systems act as powerful productivity multipliers, they also introduce significant security vulnerabilities across their input, processing, and output stages. The text highlights a top ten list of risks, such as goal hijacking, tool misuse, and supply cha...

Morgan Stanley is at the forefront of the financial services' AI revolution. By implementing a sophisticated AI Engine and agentic workflows, the firm is significantly enhancing its advisory services and operational efficiency. Strategic Pillars: Empowering Advisors : Tools like AskResearchGPT allow advisors to query thousands of pages of proprietary research, delivering synthesized insights i...

Inside the Softmax: A New Frontier in LLM Hallucination Detection Traditional Large Language Models (LLMs) often generate factual errors, making them less reliable for critical tasks. This new research offers a breakthrough by reinterpreting the final softmax layer as an Energy-Based Model (EBM). The researchers localise precise answer tokens and test for hallucinations without the need for traine...

The convergence of artificial intelligence and cognitive science introduces profound implications for the future of human-machine interaction. As AI evolves to interface directly with biological neural networks, establishing a robust framework for Neuro-Digital Semantic Governance is imperative. Strategic Pillars: Ethical Foundations : Addressing the critical need for cognitive privacy, ensuring A...

These sources explore the evolving landscape of open-source Vision-Language Models (VLMs) and their specialized application in Optical Character Recognition (OCR). The first source introduces DeepSeek-OCR, a model utilizing a unique DeepEncoder architecture to compress high-resolution visual data into a minimal number of tokens while maintaining high text extraction accuracy. It highlights the pot...

Goldman Sachs has established a strategic infrastructure to integrate generative AI across its global financial and engineering operations. By providing its entire workforce with secure access to advanced LLMs, the firm is setting a new benchmark for institutional AI adoption. Strategic Pillars: Developer Efficiency : Deploying coding assistants and autonomous agents like Devin has resulted in a 2...

These sources provide a comprehensive overview of the Large Language Model (LLM) landscape in 2026, focusing on the technical analysis and practical deployment of open-source and small language models (SLMs). One research paper investigates how quantization—a method of compressing models by reducing numerical precision—affects internal reliability and neuron behavior across various architectures....

These sources examine the  transformative integration of Generative AI  across the global banking and financial services landscape. While the technology promises to  revolutionize operational efficiency, risk management, and hyper-personalized customer experiences , institutions remain cautious regarding  data privacy, ethical biases, and significant infrastructure costs . Major banks are increasi...

These sources explore the  digital transformation  currently reshaping the  equipment leasing and finance industry . Research from the  Equipment Leasing & Finance Foundation  highlights how lenders are adopting  automation, artificial intelligence, and paperless workflows  to meet rising customer demands for speed and transparency. While  digitalization  offers significant benefits like  cost...

Standard AI agents currently operate like volatile memory , meaning they lose all progress once a session concludes. While technologies like Retrieval Augmented Generation allow agents to read existing data, they lack a dedicated method for saving original work or code. Agentic storage addresses this limitation by providing a persistent layer where autonomous systems can permanently store and mana...

Standard AI agents currently operate like volatile memory , meaning they lose all progress once a session concludes. While technologies like Retrieval Augmented Generation allow agents to read existing data, they lack a dedicated method for saving original work or code. Agentic storage addresses this limitation by providing a persistent layer where autonomous systems can permanently store and mana...

The provided text outlines two primary frameworks,  A2A  and  MCP , designed to solve the communication challenges inherent in isolated AI agents.  A2A (Agent-to-Agent)  is an open protocol that facilitates direct interaction between different AI entities, allowing them to exchange tasks and status updates regardless of their underlying vendors. In contrast, the  Model Context Protocol (MCP)  prov...

The provided text outlines two primary frameworks,  A2A  and  MCP , designed to solve the communication challenges inherent in isolated AI agents.  A2A (Agent-to-Agent)  is an open protocol that facilitates direct interaction between different AI entities, allowing them to exchange tasks and status updates regardless of their underlying vendors. In contrast, the  Model Context Protocol (MCP)  prov...

The provided text outlines two primary frameworks,  A2A  and  MCP , designed to solve the communication challenges inherent in isolated AI agents.  A2A (Agent-to-Agent)  is an open protocol that facilitates direct interaction between different AI entities, allowing them to exchange tasks and status updates regardless of their underlying vendors. In contrast, the  Model Context Protocol (MCP)  prov...

This source outlines the OWASP Top 10 vulnerabilities specifically targeting Large Language Models (LLMs) and AI systems. It highlights critical risks such as prompt injection , where attackers manipulate model instructions, and sensitive information disclosure , which can lead to data breaches. The text also warns against supply chain vulnerabilities and model poisoning , emphasizing that the dat...

This source outlines the OWASP Top 10 vulnerabilities specifically targeting Large Language Models (LLMs) and AI systems. It highlights critical risks such as prompt injection , where attackers manipulate model instructions, and sensitive information disclosure , which can lead to data breaches. The text also warns against supply chain vulnerabilities and model poisoning , emphasizing that the dat...

This source outlines the OWASP Top 10 vulnerabilities specifically targeting Large Language Models (LLMs) and AI systems. It highlights critical risks such as prompt injection , where attackers manipulate model instructions, and sensitive information disclosure , which can lead to data breaches. The text also warns against supply chain vulnerabilities and model poisoning , emphasizing that the dat...

Standard AI agents currently operate like volatile memory , meaning they lose all progress once a session concludes. While technologies like Retrieval Augmented Generation allow agents to read existing data, they lack a dedicated method for saving original work or code. Agentic storage addresses this limitation by providing a persistent layer where autonomous systems can permanently store and mana...

Modern AI development is shifting away from the "super agent" myth toward collaborative systems where multiple specialized agents work together on specific tasks. To ensure safety and efficiency, developers must avoid over-privilege and "super agency" by enforcing high cohesion , which limits each agent's access to the absolute minimum required for its role. This architectu...

Modern AI development is shifting away from the "super agent" myth toward collaborative systems where multiple specialized agents work together on specific tasks. To ensure safety and efficiency, developers must avoid over-privilege and "super agency" by enforcing high cohesion , which limits each agent's access to the absolute minimum required for its role. This architectu...

Modern AI development is shifting away from the "super agent" myth toward collaborative systems where multiple specialized agents work together on specific tasks. To ensure safety and efficiency, developers must avoid over-privilege and "super agency" by enforcing high cohesion , which limits each agent's access to the absolute minimum required for its role. This architectu...

RAG vs. Long Context: The Future of Model ArchitectureLarge language models are inherently limited by training cut-off dates and a lack of access to private data, necessitating methods to inject external information. The provided text compares two primary strategies for overcoming these hurdles: Retrieval Augmented Generation (RAG) and Long Context windows. RAG acts as an engineering-heavy approac...

RAG vs. Long Context: The Future of Model ArchitectureLarge language models are inherently limited by training cut-off dates and a lack of access to private data, necessitating methods to inject external information. The provided text compares two primary strategies for overcoming these hurdles: Retrieval Augmented Generation (RAG) and Long Context windows. RAG acts as an engineering-heavy approac...

The provided text emphasizes that human oversight is a critical requirement for integrating AI agents into professional environments. While these tools excel at executing tasks rapidly, they often lack the contextual awareness to understand the long-term consequences of their actions. Without a human-in-the-loop architecture , agents may prioritize narrow goals, such as speed, while inadvertently...