… You use ChatGPT or Claude every day, and nobody at your company built anything for it. So why does your tech team want a platform? Look at the two ways your company uses AI.

In the first, you’re the user. You type into ChatGPT or Claude. OpenAI or Anthropic built that product, picked the model and decided what it may say.

… In the second, your customer is the user. They type into an AI feature your team built inside your product. …Once several teams build features like these, they all need the same things around their model calls. That shared layer sits between all your AI features and all the models they call. — Read More

𝗢𝗻𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗶𝗮𝗴𝗿𝗮𝗺 𝗧𝗵𝗮𝘁 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝘀 𝗘𝘃𝗲𝗿𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁

Every AI agent you’ve ever used follows the same pattern.

ChatGPT. Claude. Copilot. Devin. Custom agents built with LangGraph or CrewAI. Even the autonomous multi-agent systems running inside enterprises right now.

Strip away the branding and the frameworks and they all share one architecture loop:

𝗣𝗲𝗿𝗰𝗲𝗶𝘃𝗲 → 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿 → 𝗧𝗵𝗶𝗻𝗸 → 𝗣𝗹𝗮𝗻 → 𝗔𝗰𝘁 → 𝗢𝗯𝘀𝗲𝗿𝘃𝗲 → 𝗟𝗼𝗼𝗽

— Read More

We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call this the Matthew Effect in RL for LLMs, after the phenomenon of cumulative advantage from economics and network science summarized as “the rich get richer”. The naive explanation is that hard problems require more compute to find a solution. We argue that modern RL methods are exacerbating the issue by wasting too much compute on easy problems and instead should dynamically reallocate how they use compute. We introduce Never Give Up (NGU), a simple adaptive sampling method that keeps generating samples for a problem until one is correct. By leveraging asynchronous RL, this naturally uses fewer samples to filter out easy problems and allocates more compute to solving harder problems. We investigate the design choices that affect NGU, such as off-policy robustness, and develop a set of best practices. On the math benchmark Deepscaler, NGU improves performance per compute, especially on harder problems. On a recent coding task, Manufactoria, standard GRPO with a per-test reward fails to fully solve problems that have a range of easy and difficult tests. NGU iteratively improves, solving harder and harder tests, until it learns to fully solve coding problems. — Read More

#architecture

Mechanistic interpretability is the science of “reading an AI’s mind”.

Large language models are “grown, not built”. Researchers run training data through a neural network. Eventually this creates a working AI; nobody really knows how.

But a neural network is just a set of simulated neurons on a computer. The person with the computer can see the neurons, the connections between them, and which ones activate when the AI answers questions. So it seems like it should be possible to “reverse engineer” the AI.  … Unfortunately this is very hard.  — Read More

There’s so much talk about new ways of working with agent engineering supported workflows. New models are independently creating new metrics and transformations, finding gaps in the business data, reviewing the SQL they write, and verifying everything works with your data platform.

All of it is autonomous, so one might say, what then is left for us humans to do in the data work context? Many are defaulting to adding or curating context, ergo the rise of a context layer. I see even more talks about added Ontologies. Maybe you ask yourself, what is that even? Do we need all of it?

This article is a primer about the context layer, the difference between a classical semantic layer contained in every BI tool, and an external semantic layer. — Read More

We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon.

This is not a typical domain-specific agent. Its novelty comes from integrating two layers:

— A structured, auditable knowledge architecture separates what the agent knows from how it reasons.
— A self-improvement loop then compiles expert feedback into verified, regression-tested updates without model retraining. 

Together, these two layers turn one-off expert corrections into permanent, compounding institutional memory, and the pattern is designed to generalize to other domains governed by retrievable text rather than model weights. — Read More

Today, we’re sharing Solaris: the first model in a new family of AI systems we call Interface World Models. Solaris starts with a question: what happens when an operating system generates apps and websites as you use them?

Every operating system, from early terminals to Linux and macOS, has dictated what’s rendered on screen and what happens when a person or program acts on it. Applications get built on top, and stay fixed until someone pushes an update. Solaris instead renders that layer directly. It’s a real-time interactive model that generates the interface itself, frame by frame. Every frame is synthesized as you interact, allowing the interface to respond continuously to your actions. — Read More

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent’s organizational capacity. Augmenting one agent’s capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system level. We call this System Intelligence: an agent system’s ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole pursuing a shared objective. Achieving it requires more than adding agents; it demands explicit structures to organize work, coordinate heterogeneous agents, and maintain evolving execution states. We introduce Graph Engineering, an emerging paradigm for next-generation agent systems. Unlike prior paradigms that mainly optimize individual interactions or agent-level behavior, Graph Engineering constructs explicit, dynamic, evolving graph structures representing tasks, agents, and system states. These abstractions provide a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution. We systematically review the principles, methodologies, and applications of Graph Engineering for LLM agents. Related papers, open-source data, and projects are collected at this https URL. — Read More

Sometime around Christmas 2025, AI engineers noticed a change in agents. They started to work! It’s hard to pin down exactly why. Maybe we finally had holiday downtime to try the newest agents with the newest models. Maybe the models had crossed some capability threshold. Maybe the wrappers around the models had matured.

What I’ll argue in this post is that it was the confluence of the last two. The model and the harness improving together and then their curves of improvement crossing at the right moment. And that dynamic helps to explain what comes next: models keep absorbing the harness into their weights, engineers keep deleting what got absorbed, and what remains is a harness for human attention rather than for the model. — Read More

I have had some version of the same conversation with a CIO almost every day this year. It usually starts the same way. Their team has read about context layers, or seen the term in a Gartner note. They know it matters. They know it sits somewhere underneath the agents they are trying to put into production.

… So here’s the question I actually want to answer, when you strip away the branding, what is a context layer made of, and how do you tell a real one from a catalog with a new coat of paint? — Read More