As AI coding assistants become increasingly capable, a new challenge is emerging that many researchers and software engineers refer to as the “endurance gap.” While modern coding agents can generate applications, write functions, fix bugs, and produce documentation with impressive speed, their effectiveness often declines during long, complex software engineering tasks that require sustained reasoning across hundreds of interconnected steps.
This limitation has become more visible as organizations experiment with using AI not only for code generation but also for larger development projects involving architectural changes, large-scale refactoring, dependency management, testing, and production maintenance. In many cases, coding agents perform exceptionally well during the initial stages of a project but begin to lose accuracy as the complexity and duration of the task increase.
The problem stems from the fact that software engineering is rarely a sequence of isolated coding tasks. Real-world projects require maintaining context across numerous files, understanding design decisions made earlier in the process, tracking dependencies, and adapting to changing requirements. Even highly advanced AI models can struggle to preserve consistency over extended workflows, leading to mistakes that accumulate as projects grow.
Recent developments from Xiaomi suggest that the company is attempting to tackle this challenge with its MiMo Code system. According to reported results, the model demonstrates improved performance on coding tasks extending beyond 200 sequential steps, a benchmark that many existing coding agents find difficult to sustain. The focus is not simply on generating code quickly, but on maintaining coherence, accuracy, and problem-solving ability throughout lengthy development processes.
The distinction is becoming increasingly important as AI evolves from a coding assistant into a potential software engineering collaborator. Generating a small application or fixing an isolated bug may require only a short chain of reasoning. Refactoring a production system, however, often involves analyzing existing architecture, modifying multiple components, updating tests, validating compatibility, and ensuring that each change remains aligned with the broader objectives of the project.
Researchers describe this challenge as similar to the difference between sprinting and running a marathon. Many models can deliver strong performance over short distances, but maintaining reliability over extended periods requires a different set of capabilities. Long-horizon reasoning, memory management, context retention, and adaptive planning are increasingly viewed as critical requirements for the next generation of coding agents.
The endurance problem also has direct implications for enterprise adoption. Organizations evaluating AI-assisted development tools are less interested in isolated benchmark scores and more concerned with whether a system can contribute effectively to complex production environments. A model that performs well across hundreds of development steps may deliver greater value than one that excels only in short coding exercises.
Advances in this area could significantly change how software teams operate. If AI systems become capable of reliably managing long-running development tasks, they may assist with large-scale migrations, technical debt reduction, platform modernization efforts, and other projects that currently require substantial human oversight. Such capabilities could improve productivity while allowing engineers to focus on higher-level design and strategic decisions.
The race to improve coding agents is therefore shifting from raw code generation toward sustained performance and reliability. As companies compete to build more capable development tools, the ability to maintain context and reasoning across extended workflows may become one of the most important measures of real-world usefulness. The next generation of AI coding systems will likely be judged not by how quickly they can start a project, but by how effectively they can finish one.