In an impressive feat, Japanese startup Sakana AI Coding Agent ALE-Agent Recently won first place in the AtCoder Heuristic Contest (AHC058), which is a complex coding competition involving complex optimization problems – and it is a more difficult and perhaps telling challenge than benchmarks like HumanEval, which mostly test the ability to write discrete tasks, and which many AI models and agents now routinely pass with ease ("benchmark saturation").
able to The achievement with ALE-Agent signals a shift toward agents that are able to autonomously adapt 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 estimation-time scaling to generate, test, and iterate hundreds of solutions, solving a problem that typically requires deep intuition and time-consuming trial and error from human experts. It beat out over 800 human participants, including top-tier competitive programmers.
How does ALE-Agent work?
The challenge was classic in AHC058 joint optimization crisis. Participants were tasked with managing a set of machines with hierarchical relationships, such as machines that produce apples, and other machines that produce 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 "objective function" (aka the scorer), and then engineers create a software system to optimize it. These problems are extremely difficult because they cannot be solved in one step. They need exploration, strategy, and the ability to pivot when a plan isn’t working.
Human experts typically approach this using a two-step strategy. First, they use a "Greedy" Method for generating a good baseline solution (a lightweight solver that makes the best immediate choice at each step). Then, they apply "simulated annealing," A technique that takes an existing plan and makes small, random adjustments to see if the score improves. However, this standard approach is rigid. If the initial greedy scheme goes in the wrong direction, simulated annealing can rarely correct it because it only looks for local improvements in the faulty region of the solution space.
The innovation of ALE-Agent was turning this static initialization tool into a dynamic reconstruction engine. Instead of relying on immediate value, the agent independently derived a concept he called "virtual power." This assigned values to components that were not yet operational, treating them as if they already had the value. By valuing potential future assets rather than just current assets, the agent took advantage of "compound interest effect," a concept that it clearly identified internal log. Basically, it can look a few steps ahead and reason about the future rather than just looking at the immediate feedback it gets from its surroundings.
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 text "insight" By considering each test. It gathers this knowledge to prevent falling back on previously failed strategies and creates a working memory that allows it to look a few steps ahead rather than reacting to immediate feedback.
Furthermore, the agent integrated greedy methods directly into the simulated annealing step to avoid getting stuck in local optima, and used high-speed reconstruction to instantly remove and reconstruct large parts of the solution.
From coding to enterprise customization
This breakthrough fits directly into existing enterprise workflows where scoring functions are already available. Currently, companies rely on scarce engineering talent to write optimization algorithms. ALE-Agent envisions a future where humans define "counter" (i.e., business logic and goals) and the agent handles the technical implementation.
This transforms the operational constraint from engineering capability to metric clarity. If an enterprise can measure a goal, the agent can optimize it. It 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 customization. "This enables a future where non-technical customers can interact directly with an agent, overcoming business obstacles in real time until they get the desired output," He 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 collaboration with enterprises.
At the same time, the team is already looking ahead "self-rewriting" Agent. These future agents could define their own scorers, making them viable for ill-defined problems where human experts struggle to formulate clear initial metrics.
price of intelligence
Running ALE-Agent was not cheap. The four-hour operation incurred approximately $1,300 in computation costs, involving more than 4,000 reasoning calls to the models. GPT-5.2 And gemini 3 pro. Although this price point may 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 that expect to reduce costs may be missing the strategic picture. While the cost of tokens is falling, total spending may actually increase as companies compete for better answers, a concept known as jevons paradox.
"While smart algorithms will increase efficiency, the primary value of AI is its ability to explore vast solution spaces," Said the Sakana AI team. "As anticipated costs decline, rather than simply banking the savings, enterprises will choose to leverage that affordability to conduct even deeper, broader searches to find better solutions."
This experiment highlights the enormous value still to be unlocked through inference-time scaling techniques. As AI systems gain the ability to handle complex reasoning tasks over longer contexts, create better scaffolding and allocate larger budgets "time to think" Allows agents to compete against top human experts.
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