5 posts tagged with "harness-engineering"

On September 6, 2026, three days after GPT-6 Astra shipped, OpenAI chief scientist Jakub Pachocki published a rare essay titled "An Alien Mind." The tone was unusually grave for the genre: no lab, including his own, has solved alignment and monitoring well enough to justify scaling at maximum speed. Models keep getting more capable and harder to fully understand or supervise. What should worry you isn't what they can't do yet — it's that they've started participating in something specific: improving AI itself. That's the subject of this post.

The alarm isn't coming from nowhere. In May, Dario Amodei said something that carried real weight: Claude is helping design Claude. Not "Claude writes code" — Claude's own suggestions are shaping the architecture of the next Claude.

That same month, Yuandong Tian brought his company out of stealth. He's Meta FAIR's former research director, known for ELF OpenGo, an open-source system that replicated AlphaZero's core ideas, and he'd already left Meta the year before. This time he surfaced with seven co-founders, including Richard Socher — who led AI research at Salesforce and now serves as CEO. The company is called Recursive, also known as Recursive Superintelligence: $650M raised, $4.65B valuation, personal backers including Jensen Huang and Lisa Su.

Three months later, in August, Jeff Dean left too. Google employee number 30, 27 years in, he took Sanjay Ghemawat, Oriol Vinyals, and Quoc Le with him to co-found Discovery Loop, with an explicit mandate to run thousands of experiments in parallel and pursue recursive self-improvement directly.

Tian's company had already delivered its first result: the same automated research system beat human GPU experts on a kernel-optimization benchmark. Google's own AI co-scientist got a paper through Nature peer review. And a Princeton study found the same class of models fail at exactly this kind of task.

This is RSI, recursive self-improvement. I've spent the last month going deep on it for my own knowledge base, and it's the first AI topic in a while where I came out the other side less certain than when I went in. That uncertainty is the actual finding. This post is the map: what RSI is, who's building it, what evidence exists on both sides, and why the smartest people in the field can't agree on whether it's happening.

Claude Tag has been the AI story lately. Anthropic gave Claude a persistent identity inside Slack — its own account, its own memory of what a channel cares about, the ability to notice a problem and start fixing it without anyone asking. Andrej Karpathy called it the third major redesign of LLM UI/UX: first the LLM was a website you visited, then an app you downloaded, now a persistent, asynchronous entity with org-wide tools and context, working alongside a team of humans.

I've been writing about a version of this problem for months. In OpenSpec + Harness, Then We Added Engineers, I described what breaks when individual AI acceleration hits a team: spec quality becomes the bottleneck, PR review bandwidth becomes the bottleneck, shared files become a contention point. Claude Tag is the next stop on that same line. It just takes a different road than the one I built.

One engineer running Claude Code well is not the finish line. What's still unsolved is how a group of people use it together. Individual output is up. Team-level delivery time hasn't made the same jump. That gap is what the next round of Harness Engineering has to close.

At the 2026 Agentic AI Summit at UC Berkeley, five people from five unrelated fields said the same sentence, independently, within two days of each other.

• Oriol Vinyals, VP of Research at Google DeepMind, on recursive self-improvement: "Evaluating this truly in an agentic way might take some effort. Currently it's the all-automation bottleneck."
• Wang Mengdi, professor of electrical and computer engineering at Princeton, on AI for science: "Verification has become the major bottleneck for scaling any AI models."
• Adarsh Hiremath, co-founder and co-CEO of Mercor, on enterprise deployment: "Evals in a large part are the bottleneck to successfully deploying agents in a company."
• Sergei Gukov, professor of theoretical physics and mathematics at Caltech, on mathematical discovery: "Your system is going to be just as good as evaluator."
• Vincent Chen, research fellow at Snorkel AI, on measurement itself: "Our ability to measure AI has really been outpaced by our ability to develop it."

Recursive self-improvement, natural science, enterprise software, pure mathematics, measurement theory. Five fields that share almost no vocabulary landed on the same diagnosis. That kind of convergence, across domains with no reason to be reading each other's papers, is the strongest kind of evidence a claim can get.

Gartner recorded a 1,445% increase in multi-agent consulting requests last year. In the same period, 40% of multi-agent pilots died within six months.

That gap is the thing worth understanding.

I've been building with agents long enough to know that most framework selection conversations start in the wrong place. Engineers ask "which framework?" when the prior question — do we need multi-agent at all? — hasn't been answered. The data on that prior question is more interesting than most of the framework benchmarks.

Two things landed in the same week and pointed at the same idea.

Martin Fowler named Harness Engineering as the core software engineering work of the AI era at FOSE Europe — specifically the Guide/Sensor model: Guides as feedforward constraints that tell an agent what to do, Sensors as feedback detectors that tell the system when it's drifting. He added an observation worth pinning: token consumption is a proxy metric for harness quality. A better-designed harness means a cheaper, more reliable agent.

Then Lilian Weng published Harness Engineering for Self-Improvement — a systematic review of 35 papers on agent harness engineering, with a thesis that goes beyond reliability: the harness isn't just the thing that makes an agent work. It's the infrastructure through which the agent continuously improves itself.

A friend has been telling me for months that a particular company's bet on the future of software development is: systems that automatically optimize their own code and architecture. Fowler said what we should build now. Weng said what comes after. These three converged into the same question, so I'm trying to map the whole space.

This post maps Evolutionary Search — why it's the natural next step for harness engineering, what the paper landscape looks like organized by evolutionary depth, and where the hard problems actually live. The taxonomy here draws heavily on Weng's framework; I've reorganized it by depth of what gets evolved.