With the development of foundation model (FM), agentic AI systems are getting more attention, yet their inherent issues like hallucination and poor reasoning, coupled with the frequent ad-hoc nature of system design, lead to unreliable and brittle applications. Existing efforts to characterise agentic design patterns often lack a rigorous systems-theoretic foundation, resulting in high-level or convenience-based taxonomies that are difficult to implement. This paper addresses this gap by introducing a principled methodology for engineering robust AI agents. We propose two primary contributions: first, a novel system-theoretic framework that deconstructs an agentic AI system into five core, interacting functional subsystems: Reasoning & World Model, Perception & Grounding, Action Execution, Learning & Adaptation, and Inter-Agent Communication. Second, derived from this architecture and directly mapped to a comprehensive taxonomy of agentic challenges, we present a collection of 12 agentic design patterns. These patterns – categorised as Foundational, Cognitive & Decisional, Execution & Interaction, and Adaptive & Learning – offer reusable, structural solutions to recurring problems in agent design. The utility of the framework is demonstrated by a case study on the ReAct framework, showing how the proposed patterns can rectify systemic architectural deficiencies. This work provides a foundational language and a structured methodology to standardise agentic design communication among researchers and engineers, leading to more modular, understandable, and reliable autonomous systems. — Read More
Recent Updates
Castle Walls review – Prime’s first fully AI-generated show is here, and it’s a Vaseline-smeared mess
Just as the world reels from Elon Musk’s AI-powered The Odyssey remake – though “remake” feels rather generous for a seven-minute splat of synthetic sludge – another bucket of AI gunk is dropped on to our faces. For Prime Video’s AI-generated TV series Castle Walls, produced by Turkish production studio Ay Yapım using a melange of generative video tools, has arrived, billed as the first end-to-end AI-created series to premiere on a major streaming platform.
It’s certainly more epic than Musk’s production, consisting of two 17-minute episodes. Set in the 11th century, the show revolves around the historic Alamut Castle, although it’s entirely absent of any real sense of time or place. I was briefly intrigued by the opening images, with their bold palette of blues and mustard browns and yellows – but as soon as the “people” appeared, looking like Vaseline-smeared Madame Tussauds creations slowly melting in the sun, everything came crashing down. — Read More
Practical Loop Engineering
… You’ve probably heard about loop engineering. I talked about it a couple of months ago when I wrote a big blog post about it.
A loop is an autonomous, self-correcting feedback cycle where an AI agent repeatedly acts, tests its results, and adjusts its approach until a specific goal is met.
There are now basically two core primitives you can think about. In Claude Code you have a goal primitive, which can drive a single bounded task forward until you’ve got a particular goal, like a measurable finish line that’s been met. And then loop reruns on a timer or a fixed interval, so you can use it to schedule changes. — Read More
Let Decision Models Watch Your Agents
LLM agents can fail, and their actions can have real consequences.
Just like other automated systems, they need condition monitoring to detect anomalous behavior accurately and promptly.
Decision models, which are specially trained AI models that take relevant context and make quick judgments, make real-time agent anomaly detection much more feasible. — Read More
Anthropic’s moat is Claude
The bull case for Anthropic’s IPO.
Anthropic recently filed for IPO. At an estimated $1.5 to $2 trillion, it will be one of the biggest IPOs of all time, rivaling SpaceX, and possibly the biggest. For reference, Facebook’s valuation when it had its blockbuster IPO in 2012 was $104 billion, and Google’s valuation in 2004 was $23 billion. Anthropic will instantly become one of the world’s most valuable companies — Google itself is now worth $3.4 trillion, and Nvidia and Apple are both worth around $5 trillion.
This naturally has a lot of people asking: What’s the upside to buying Anthropic? — Read More
GDPx: Measuring Frontier Model Capabilities Across the Most Valuable Domains
GDPx is a benchmark that covers the most valuable domains for frontier intelligence. It aims to be the world’s best benchmark for measuring general agentic capabilities.
We believe the first trillion dollars of AI revenue will be concentrated in sectors that can deploy frontier intelligence at high ROI, not broadly diffused throughout the whole economy. Building a general-purpose benchmark based on share of GDP or the SOC/O*NET occupational taxonomy is therefore a mistake. If your goal is to track the progress of frontier models, you should focus on the domains where they will be most impactful. — Read More
The agentic web is here
In 2001, a decade after he invented the World Wide Web, Tim Berners-Lee articulated a new vision. Together with two colleagues, he published an article in the magazine Scientific American in which he outlined what he called the “Semantic Web”: an internet designed to be navigated by autonomous software agents that could address our every need, without requiring us to muck about with browsers and search engines ourselves.
… For the next two decades, then, the internet remained a place for humans that put humans first, with the odd Twitter reply bot or web scraper to round things out.
Large language models changed that. Unlike the agents that Berners-Lee was imagining, today’s LLM-based agents don’t need any tags to understand which part of the text on a clinic’s site is its address and which details the services it offers. They can search for and process text far faster than any human, and agents with multimodal capabilities can even interpret screenshots of websites and click on specific elements such as hyperlinks and drop-down menus. — Read More
Silicon Valley Is Wrong About AI, And Wall Street Is Right
Big Tech just finished another earnings season, and the capex numbers went up again. Amazon, Alphabet, Microsoft, and Meta now guide to roughly $720–745 billion of combined capital spending for calendar 2026, versus about $410 billion spent in 2025. Meanwhile, in San Francisco, the conversation is still mostly about when AGI arrives and which lab gets there first. Those two conversations are happening in different worlds, and I think one of them is a lot closer to reality than the other.
Today I want to talk about why Silicon Valley is projecting its old business model onto a technology that doesn’t behave like software. The Valley grew up on zero-marginal-cost products with monopoly economics. AI looks much more like a capital-intensive industrial utility with brutal reinvestment needs and falling unit prices. Wall Street, for all its flaws, is pricing it that way. — Read More
The Rise of Agentic Attacks: What They Look Like, and Where They’re Headed
There’s a clear trend emerging across our incident response engagements and threat hunting investigations. Wiz increasingly sees intrusions where AI agents appear to handle much of the hands-on work and potentially orchestrate the attack itself. These agents operate through harnesses that write custom tooling, inspect the results, adapt, and iterate at machine speed, while a human sets the objectives and guardrails.
These attacks can be verbose, with agents trying multiple approaches in parallel. They appear at both ends of the spectrum, from sharply targeted to broadly opportunistic. What begins with a single exposed service can expand to cloud credential theft, Kubernetes token theft, lateral movement, and bulk secret exfiltration. AI agents compress the skilled, hands-on work that such escalation once required. There is, however, a potential advantage for defenders. Current agentic attacks tend towards the noisy, leaving behind a broad trail of observable activity. The challenge is to distinguish this activity from legitimate automation and correlate individual signals into a coherent picture of an attack.
In our recent blogs, Anatomy of AI-Driven Cloud Attacks and Storm-0940 Expands Targeting of IT and Software Vendors we examined specific agentic intrusions in depth. This post takes a broader view, reviewing both private and public cases to identify recurring fingerprints of agentic activity. We also reflect on what this shift means for attribution, the scale of attacks, and the future of defense. — Read More
What Google and Reddit’s bad days have in common
On Thursday afternoon, Demis Hassabis celebrated a milestone for Google’s AI efforts: Gemini 4 Carbon, a new large language model that is currently available only to cyber defenders, topped Artificial Analysis’ Intelligence Index. After a series of delays in new model releases and a growing perception that the company is falling off the frontier, early signs suggest that Carbon marks a return to form. For Alphabet’s chief scientist, the news surely felt like a vindication.
Investors, however, are much less certain. On the same day, Google’s parent company had its worst day in history as a public company. Alphabet’s stock fell 12.6 percent, erasing around $500 billion in market value. The loss marked the second-biggest stock market plunge ever, following Nvidia’s $593 billion slide in January 2025. And it was Google’s biggest one-day decline since the great financial crisis.
Most of the selloff came from investor worries about the huge scale of Google’s AI infrastructure buildout. The company plans to spend up to $205 billion on capital expenses this year, and has promised to spend even more in 2027. And it reported free cash flow of negative $9 billion — an acceleration of a decline that began the previous quarter. — Read More
