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In an impressive feat, Japanese startup Sakana AI’s coding agent ALE-Agent recently secured first place in the AtCoder Heuristic Contest (AHC058), a complex coding competition that involves complicated optimization problems — and a more difficult and perhaps telling challenge than benchmarks like HumanEval, which mostly test the ability to write isolated functions, and which many AI models and agents now regularly pass with ease (“benchmark saturation”).
Sakana’s accomplishment with ALE-Agent hints at a shift toward agents capable of autonomously optimizing themselves to navigate and perform well in complex, dynamic systems such as enterprise software stacks, workflows, and operational environments.
In four hours, the agent used inference-time scaling to generate, test, and iterate over hundreds of solutions, solving a problem that typically requires deep intuition and time-consuming trial and error from human experts. It outperformed over 800 human participants, including top-tier competitive programmers.
How ALE-Agent works
The challenge in AHC058 was a classic combinatorial optimization problem. Participants were tasked with managing a set of machines with hierarchical relationships, such as machines that produce apples, and other machines that build those apple-producing machines. The goal was to maximize output over a fixed number of turns.
In the enterprise world, this workflow usually follows a strict pattern: a domain expert works with a client to define an “objective function” (aka the Scorer), and then engineers build a software system to optimize it. These problems are notoriously difficult because they cannot be solved in a single stage. They require exploration, strategy, and the ability to pivot when a plan isn’t working.
Human experts typically approach this using a two-stage strategy. First, they use a “Greedy” method (a lightweight solver that makes the best immediate choice at each step) to generate a decent baseline solution. Then, they apply “simulated annealing,” a technique that takes the existing plan and makes tiny, random adjustments to see if the score improves. However, this standard approach is rigid. If the initial Greedy plan heads in the wrong direction, simulated annealing can rarely fix it because it only looks for local improvements in a faulty area of the solution space.
ALE-Agent’s innovation was transforming this static initialization tool into a dynamic reconstruction engine. Instead of relying on immediate value, the agent independently derived a concept it called “Virtual Power.” It assigned values to components that were not yet operational, treating them as if they already possessed value. By valuing potential future assets rather than just current ones, the agent capitalized on the “compound interest effect,” a concept it explicitly identified in its internal logs. Basically, it could look a few steps ahead and reason about the future instead of looking at the immediate feedback it was receiving from its environment.
Crucially, the agent needed to maintain this strategy over a four-hour window without losing focus, a common failure mode known as “context drift.” In comments provided to VentureBeat, the Sakana AI team explained that the agent generates textual “insights” by reflecting on each trial. It gathers this knowledge to prevent cycling back to previously failed strategies and creates a working memory that allows it to look a few steps ahead rather than just reacting to immediate feedback.
Furthermore, the agent integrated Greedy methods directly into the simulated annealing phase to avoid getting stuck in local optima, using high-speed reconstruction to delete and rebuild large sections of the solution on the fly.
From coding to enterprise optimization
This breakthrough fits directly into existing enterprise workflows where a scoring function is already available. Currently, companies rely on scarce engineering talent to write optimization algorithms. ALE-Agent demonstrates a future where humans define the “Scorer” (i.e., the business logic and goals) and the agent handles the technical implementation.
This shifts the operational bottleneck from engineering capacity to metric clarity. If an enterprise can measure a goal, the agent can optimize it. This has direct applications in logistics, such as vehicle routing, as well as server load balancing and resource allocation.
According to the Sakana AI team, this could democratize optimization. “It enables a future where non-technical clients can interact directly with the agent, tweaking business constraints in real-time until they get the output they desire,” they said.
The Sakana AI team told VentureBeat that ALE-Agent is currently proprietary and not available for public use, and the company is currently focused on internal development and proof-of-concept collaborations with enterprises.
At the same time, the team is already looking ahead to “self-rewriting” agents. These future agents could define their own scorers, making them feasible for ill-defined problems where human experts struggle to formulate clear initial metrics.
The cost of intelligence
Running ALE-Agent was not cheap. The four-hour operation incurred approximately $1,300 in compute costs involving over 4,000 reasoning calls to models like GPT-5.2 and Gemini 3 Pro. While this price point might seem high for a single coding task, the return on investment for optimization problems is often asymmetric. In a resource-management setting, a one-time cost of a few thousand dollars can result in millions of dollars in annual efficiency savings.
However, enterprises expecting costs to simply drop might be missing the strategic picture. While the cost of tokens is falling, total spend may actually rise as companies compete for better answers, a concept known as the Jevons paradox.
“While smarter algorithms will drive efficiency, the primary value of AI is its ability to explore vast solution spaces,” the Sakana AI team said. “As inference costs fall, rather than simply banking the savings, enterprises will likely choose to leverage that affordability to conduct even deeper, broader searches to find superior solutions.”
The experiment highlights the immense value still to be unlocked through inference-time scaling techniques. As AI systems gain the ability to handle complex reasoning tasks across longer contexts, building better scaffolding and allocating larger budgets for “thinking time” allows agents to rival top human experts.
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Anthropic released Cowork on Monday, a new AI agent capability that extends the power of its wildly successful Claude Code tool to non-technical users — and according to company insiders, the team built the entire feature in approximately a week and a half, largely using Claude Code itself.
The launch marks a major inflection point in the race to deliver practical AI agents to mainstream users, positioning Anthropic to compete not just with OpenAI and Google in conversational AI, but with Microsoft’s Copilot in the burgeoning market for AI-powered productivity tools.
“Cowork lets you complete non-technical tasks much like how developers use Claude Code,” the company announced via its official Claude account on X. The feature arrives as a research preview available exclusively to Claude Max subscribers — Anthropic’s power-user tier priced between $100 and $200 per month — through the macOS desktop application.
For the past year, the industry narrative has focused on large language models that can write poetry or debug code. With Cowork, Anthropic is betting that the real enterprise value lies in an AI that can open a folder, read a messy pile of receipts, and generate a structured expense report without human hand-holding.
How developers using a coding tool for vacation research inspired Anthropic’s latest product
The genesis of Cowork lies in Anthropic’s recent success with the developer community. In late 2024, the company released Claude Code, a terminal-based tool that allowed software engineers to automate rote programming tasks. The tool was a hit, but Anthropic noticed a peculiar trend: users were forcing the coding tool to perform non-coding labor.
According to Boris Cherny, an engineer at Anthropic, the company observed users deploying the developer tool for an unexpectedly diverse array of tasks.
“Since we launched Claude Code, we saw people using it for all sorts of non-coding work: doing vacation research, building slide decks, cleaning up your email, cancelling subscriptions, recovering wedding photos from a hard drive, monitoring plant growth, controlling your oven,” Cherny wrote on X. “These use cases are diverse and surprising — the reason is that the underlying Claude Agent is the best agent, and Opus 4.5 is the best model.”
Recognizing this shadow usage, Anthropic effectively stripped the command-line complexity from their developer tool to create a consumer-friendly interface. In its blog post announcing the feature, Anthropic explained that developers “quickly began using it for almost everything else,” which “prompted us to build Cowork: a simpler way for anyone — not just developers — to work with Claude in the very same way.”
Inside the folder-based architecture that lets Claude read, edit, and create files on your computer
Unlike a standard chat interface where a user pastes text for analysis, Cowork requires a different level of trust and access. Users designate a specific folder on their local machine that Claude can access. Within that sandbox, the AI agent can read existing files, modify them, or create entirely new ones.
Anthropic offers several illustrative examples: reorganizing a cluttered downloads folder by sorting and intelligently renaming each file, generating a spreadsheet of expenses from a collection of receipt screenshots, or drafting a report from scattered notes across multiple documents.
“In Cowork, you give Claude access to a folder on your computer. Claude can then read, edit, or create files in that folder,” the company explained on X. “Try it to create a spreadsheet from a pile of screenshots, or produce a first draft from scattered notes.”
The architecture relies on what is known as an “agentic loop.” When a user assigns a task, the AI does not merely generate a text response. Instead, it formulates a plan, executes steps in parallel, checks its own work, and asks for clarification if it hits a roadblock. Users can queue multiple tasks and let Claude process them simultaneously — a workflow Anthropic describes as feeling “much less like a back-and-forth and much more like leaving messages for a coworker.”
The system is built on Anthropic’s Claude Agent SDK, meaning it shares the same underlying architecture as Claude Code. Anthropic notes that Cowork “can take on many of the same tasks that Claude Code can handle, but in a more approachable form for non-coding tasks.”
The recursive loop where AI builds AI: Claude Code reportedly wrote much of Claude Cowork
Perhaps the most remarkable detail surrounding Cowork’s launch is the speed at which the tool was reportedly built — highlighting a recursive feedback loop where AI tools are being used to build better AI tools.
During a livestream hosted by Dan Shipper, Felix Rieseberg, an Anthropic employee, confirmed that the team built Cowork in approximately a week and a half.
Alex Volkov, who covers AI developments, expressed surprise at the timeline: “Holy shit Anthropic built ‘Cowork’ in the last… week and a half?!”
This prompted immediate speculation about how much of Cowork was itself built by Claude Code. Simon Smith, EVP of Generative AI at Klick Health, put it bluntly on X: “Claude Code wrote all of Claude Cowork. Can we all agree that we’re in at least somewhat of a recursive improvement loop here?”
The implication is profound: Anthropic’s AI coding agent may have substantially contributed to building its own non-technical sibling product. If true, this is one of the most visible examples yet of AI systems being used to accelerate their own development and expansion — a strategy that could widen the gap between AI labs that successfully deploy their own agents internally and those that do not.
Connectors, browser automation, and skills extend Cowork’s reach beyond the local file system
Cowork doesn’t operate in isolation. The feature integrates with Anthropic’s existing ecosystem of connectors — tools that link Claude to external information sources and services such as Asana, Notion, PayPal, and other supported partners. Users who have configured these connections in the standard Claude interface can leverage them within Cowork sessions.
Additionally, Cowork can pair with Claude in Chrome, Anthropic’s browser extension, to execute tasks requiring web access. This combination allows the agent to navigate websites, click buttons, fill forms, and extract information from the internet — all while operating from the desktop application.
“Cowork includes a number of novel UX and safety features that we think make the product really special,” Cherny explained, highlighting “a built-in VM [virtual machine] for isolation, out of the box support for browser automation, support for all your claude.ai data connectors, asking you for clarification when it’s unsure.”
Anthropic has also introduced an initial set of “skills” specifically designed for Cowork that enhance Claude’s ability to create documents, presentations, and other files. These build on the Skills for Claude framework the company announced in October, which provides specialized instruction sets Claude can load for particular types of tasks.
Why Anthropic is warning users that its own AI agent could delete their files
The transition from a chatbot that suggests edits to an agent that makes edits introduces significant risk. An AI that can organize files can, theoretically, delete them.
In a notable display of transparency, Anthropic devoted considerable space in its announcement to warning users about Cowork’s potential dangers — an unusual approach for a product launch.
The company explicitly acknowledges that Claude “can take potentially destructive actions (such as deleting local files) if it’s instructed to.” Because Claude might occasionally misinterpret instructions, Anthropic urges users to provide “very clear guidance” about sensitive operations.
More concerning is the risk of prompt injection attacks — a technique where malicious actors embed hidden instructions in content Claude might encounter online, potentially causing the agent to bypass safeguards or take harmful actions.
“We’ve built sophisticated defenses against prompt injections,” Anthropic wrote, “but agent safety — that is, the task of securing Claude’s real-world actions — is still an active area of development in the industry.”
The company characterized these risks as inherent to the current state of AI agent technology rather than unique to Cowork. “These risks aren’t new with Cowork, but it might be the first time you’re using a more advanced tool that moves beyond a simple conversation,” the announcement notes.
Anthropic’s desktop agent strategy sets up a direct challenge to Microsoft Copilot
The launch of Cowork places Anthropic in direct competition with Microsoft, which has spent years attempting to integrate its Copilot AI into the fabric of the Windows operating system with mixed adoption results.
However, Anthropic’s approach differs in its isolation. By confining the agent to specific folders and requiring explicit connectors, they are attempting to strike a balance between the utility of an OS-level agent and the security of a sandboxed application.
What distinguishes Anthropic’s approach is its bottom-up evolution. Rather than designing an AI assistant and retrofitting agent capabilities, Anthropic built a powerful coding agent first — Claude Code — and is now abstracting its capabilities for broader audiences. This technical lineage may give Cowork more robust agentic behavior from the start.
Claude Code has generated significant enthusiasm among developers since its initial launch as a command-line tool in late 2024. The company expanded access with a web interface in October 2025, followed by a Slack integration in December. Cowork is the next logical step: bringing the same agentic architecture to users who may never touch a terminal.
Who can access Cowork now, and what’s coming next for Windows and other platforms
For now, Cowork remains exclusive to Claude Max subscribers using the macOS desktop application. Users on other subscription tiers — Free, Pro, Team, or Enterprise — can join a waitlist for future access.
Anthropic has signaled clear intentions to expand the feature’s reach. The blog post explicitly mentions plans to add cross-device sync and bring Cowork to Windows as the company learns from the research preview.
Cherny set expectations appropriately, describing the product as “early and raw, similar to what Claude Code felt like when it first launched.”
To access Cowork, Max subscribers can download or update the Claude macOS app and click on “Cowork” in the sidebar.
The real question facing enterprise AI adoption
For technical decision-makers, the implications of Cowork extend beyond any single product launch. The bottleneck for AI adoption is shifting — no longer is model intelligence the limiting factor, but rather workflow integration and user trust.
Anthropic’s goal, as the company puts it, is to make working with Claude feel less like operating a tool and more like delegating to a colleague. Whether mainstream users are ready to hand over folder access to an AI that might misinterpret their instructions remains an open question.
But the speed of Cowork’s development — a major feature built in ten days, possibly by the company’s own AI — previews a future where the capabilities of these systems compound faster than organizations can evaluate them.
The chatbot has learned to use a file manager. What it learns to use next is anyone’s guess.