Tag: llm

Agents, AILeave a comment

Agentic AI: State of Play & Emerging Risks — May 2026

Colin Henderson

Here is result of research on Agentic State of play outlining currently understood risks which could develop into issues. In fact there are multiple indications of Agent AI deployments that will be cancelled due to the evolving landscape of risks. Financial Services in particular are seeing gaps in compliance and regulatory areas as protocols which assumed human employee engagement bump up against Agents which will act on what they observe, and have no way to act on what they cannot see. My take is that insufficient attention is being paid to formal and informal data linkages. Prepared for Splunk session … Continue reading Agentic AI: State of Play & Emerging Risks — May 2026

AI

Two significant and related stories breaking simultaneously — and they illuminate a genuine fault line in the AI industry

Colin Henderson

Two significant and related stories breaking simultaneously — and they illuminate a genuine fault line in the AI industry. source: Claude AI Anthropic vs. the Pentagon The lawsuit stems from the Trump administration’s decision to label Anthropic a “supply chain risk” — a designation normally reserved for companies associated with foreign adversaries. The trigger was Amodei’s refusal to grant the DoD unrestricted access to Claude. Anthropic’s two hard positions: it didn’t want Claude used for mass surveillance of Americans, and didn’t believe it was ready to power fully autonomous weapons with no human in the targeting and firing loop. Anthropic … Continue reading Two significant and related stories breaking simultaneously — and they illuminate a genuine fault line in the AI industry

AI, AI_factories, AI_research, Infrastructure

A comprehensive comparison between ‘compute’ and ‘inference’ in AI

Colin Henderson

An integrated analysis of technological, infrastructural, and operational factors including AI Factories, Data Centres, development practices, chips, and overall infrastructure. The following report—structured in APA format—presents a formal, detailed comparative overview, incorporating relevant business, academic, and technical sources.Source: Perplexity.ai Compute and Inference: Definitions and Context Compute generally refers to the computational resources required for both training and running AI models, whereas inference is the process by which a trained model makes predictions or decisions on new data. Compute is foundational for both the intensive process of AI model training and the comparatively lightweight process of model inference. AI inference utilizes … Continue reading A comprehensive comparison between ‘compute’ and ‘inference’ in AI

AI

The difference between using Retrieval Augmented Generation and Agents?

Colin Henderson

A thought provoking piece from Shahab Anbarjafari • Professor, Author, Public Speaker I have been discussing with a few CTOs and CIOs, all sharp, no-nonsense leaders. The question they tossed at me sounded simple: “Shahab, what’s the difference between using Retrieval Augmented Generation (hashtag#RAG) and using hashtag#Agents?” But I could sense their real curiosity—like they were trying to understand a new dimension of AI that could define the future of their organizations. In that moment, I felt like I was back at the dawn of deep learning, where we realized that neural networks were more than just pattern-matchers. They could … Continue reading The difference between using Retrieval Augmented Generation and Agents?