The main problem with standard RAG systems isn’t the retrieval or the generation. It’s that nothing sits in the middle deciding whether the retrieval was actually good enough before the generation happens.
Standard RAG is a pipeline where information flows in one direction, from query to retrieval to response, with no checkpoint and no second chance. This works fine for simple questions with obvious answers.
However, the moment a query gets ambiguous, or the answer is spread across multiple documents, or the first retrieval pulls back something that looks good but isn’t, RAG starts losing value.
Agentic RAG attempts to fix this problem. It is based on a single question: what if the system could pause and think before answering? — Read More
Recent Updates
The LiteLLM Supply Chain Attack: A Complete Technical Breakdown Of The AI Ecosystem’s Darkest Hour
On March 24, 2026, the artificial intelligence development community experienced an unprecedented security catastrophe. LiteLLM, an essential open-source Python library used to route and manage API calls across hundreds of large language models, was weaponized in a highly sophisticated supply chain attack. Threat actors known as TeamPCP successfully published two malicious versions of the package (1.82.7 and 1.82.8) directly to the Python Package Index (PyPI).
With LiteLLM averaging 97 million monthly downloads and serving as a foundational dependency for industry titans like Stripe, Netflix, and Google alongside major AI frameworks such as CrewAI, DSPy, and MLflow, the magnitude of this compromise is staggering. — Read More
The Death of model.fit(): What Data Scientists Actually Do in the Age of AI Agents
A few months ago, I joined a team building two AI-agent products.
My first week, I opened a Jupyter notebook out of habit. Then I closed it. There was no training set, no features to engineer, no model.fit(X_train, y_train) waiting to be called. The agents orchestrated foundation models. The “intelligence” came from a model someone else trained. The entire codebase was TypeScript. No notebooks, no model, no Python. The toolbox I’d spent years filling was, on its surface, irrelevant.
So what, exactly, was I supposed to do?
The answer turned out to be hiding in a simple framework.
Every AI agent has three layers. The foundation model provides raw intelligence. The engineering provides the body: tools, APIs, orchestration, and product surfaces. But the behavior of the agent – what it actually does when a user shows up – is shaped by the context, prompts, policies, schemas, and guardrails that surround the model. That’s the brain of the system. Not the neural network itself, but the cognitive architecture built on top of it.
Someone needs to own the quality of that brain; to make it legible, to understand its failure modes, measure its consistency, map its weaknesses, and create the feedback loops that systematically make it smarter. That someone, it turns out, is the data scientist. Not as a model trainer, but as the team’s methodologist. — Read More
Future Casting the Modern Data Stack
After writing an article a few years ago called “Big Data is Dead,” it feels a bit clichéd to call things “dead.” So I won’t say any such thing about the Modern Data Stack. It does, however, appear very, very sleepy. Someone should go and poke it with a stick.
The Modern Data Stack – deceased or just drowsy?
While we’re all dead in the long run, one thing that is different now is that AI is bringing the “long run” a lot closer than it has ever been. In the last couple of years, AI has forever changed a number of professions that were once thought to be safe from disruption. From art to software engineering, AI is changing how people get things done, and changing things much faster than you’d expect.
… The interesting question to me is, “What comes next?” If we assume models continue to get better, companies capitalize on the opportunities, things get tied together in a nice bow, what does the world look like? What could it look like? Let’s start with what we know. — Read More
Announcing Arm AGI CPU: The silicon foundation for the agentic AI cloud era
Today, Arm is announcing the Arm AGI CPU, a new class of production-ready silicon built on the Arm Neoverse platform and designed to power the next generation of AI infrastructure.
For the first time in our more than 35-year history, Arm is delivering its own silicon products – extending the Arm Neoverse platform beyond IP and Arm Compute Subsystems (CSS) to give customers greater choice in how they deploy Arm compute – from building custom silicon to integrating platform-level solutions or deploying Arm-designed processors. It reflects both the rapid evolution of AI infrastructure and growing demand from the ecosystem for production-ready Arm platforms that can be deployed at pace and scale. — Read More
App Store | Age of Agent
The App Store Won’t Survive the Age of Agents
When Steve Jobs launched the iPhone in 2007, there was no App Store. His plan was for developers to build web apps accessed through Safari. That lasted about a year. Developers demanded native access, and in 2008 Apple launched the App Store — bundling discovery, distribution, trust, and payment into a single controlled layer.
That bundle has generated hundreds of billions of dollars. But it was built for humans who browse, tap, and swipe. AI agents don’t do any of that. And this mismatch is about to reshape the platform economy. — Read More
Beyond Analytics: The Silent Collection of Commercial Intelligence byTikTok and Meta Ad Pixels
TikTok and Meta’s tracking pixels are quietly harvesting personal data, granular checkout interactions, and detailed commerce intelligence from the websites that implement them. The collection is going far beyond what ad attribution requires, creating serious privacy compliance risks and competitive disadvantages for the businesses involved.
Jscrambler conducted a runtime analysis of the ad pixels used by TikTok and Meta on actual websites, revealing that their default behavior requires immediate attention from every organization that employs them. The analysis focused on large companies in the retail, hospitality, and healthcare sectors. However, it’s worth noting that most businesses with an online presence use these tracking pixels on their websites. — Read More
To Thrive Today, You Have to Become An Agentic Deployment Expert. But So, So Few Actually Are.
Phase one: most of 2023. You had to be technical. The models were there but they hallucinated constantly. You needed to be deeply technical to get anything useful out of a raw LLM API. Most of us — myself included — weren’t equipped. I remember being at SaaStr Annual 2023, talking with David Sacks, asking how he was thinking about AI at Craft. He said they wanted 80% of investments to be AI. I asked to see the great ones already in market. His answer: they’re all proof of concepts. We’re all in anyway. That was the right call if you were investing at the LLM layer. I wasn’t smart enough to play there, let alone deploy AI B2B agents then.
Phase two: 2024 into early 2025: the weird prompt engineer era. You could torture these tools into doing something useful, but you had to craft these elaborate, convoluted prompts that made no sense to anyone else. “Prompt engineer” became the hottest job on the planet for about a year. That job is now dead.
Phase three — which is right now — is the era where ordinarily smart generalists can make AI agents and AI tools do genuinely magical and useful things. No contorted prompts. No engineering degree. Just software deployment skills you probably already have. Some of it is the profound leap forward of Opus 4.5+. Some of it is the agentic products themselves just have gotten better. It’s both. It’s now. — Read More
Designing Agentic AI Systems
How do you build an agentic system that works? And how do you spot potential problems during development that can snowball into massive headaches for future you when they go into production?
To answer these questions, you need to break agentic systems into three parts: tools, reasoning, and action. Each layer comes with its own challenges. Mistakes in one layer can ripple through the others, causing failures in unexpected ways. Retrieval functions might pull irrelevant data. Poor reasoning can lead to incomplete or circular workflows. Actions might misfire in production.
An agentic system is only as strong as its weakest link and this guide will show you how to design systems that avoid these pitfalls. The goal: build agents that are reliable, predictable, and resilient when it matters most. Read More:
Part 1 – Architecture
Part 2 – Modularity
Part 3 – Agent 2 Agent Interactions
Part 4 – Data & RAG
Part 5 – Vectorize MCP
Japan’s Team Mirai Uses Tech to Bolster Democracy, Not Undermine It
Japan’s election last month and the rise of the country’s newest and most innovative political party, Team Mirai, illustrates the viability of a different way to do politics.
In this model, technology is used to make democratic processes stronger, instead of undermining them. It is harnessed to root out corruption, instead of serving as a cash cow for campaign donations.
Imagine an election where every voter has the opportunity to opine directly to politicians on precisely the issues they care about. They’re not expected to spend hours becoming policy experts. Instead, an AI Interviewer walks them through the subject, answering their questions, interrogating their experience, even challenging their thinking. – Read More
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