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Training an Agentic Router for Optimal Cost-Performance on SWE Tasks | Applied Compute

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On most enterprise tasks, model quality is not a scalar. One model is better at long-horizon repository exploration. Another is better at small, surgical patches. A third has stronger general reasoning but higher latency or cost. The right model depends on the task, the environment, and the failure mode that matters. This is especially apparent in agentic software engineering, where every task unfolds as a trajectory. The model reads files, searches the repository, forms a hypothesis, edits code, runs tests, and decides when to stop. The best model for one issue may be the wrong model for the next one. Routing each task to the model best suited to solve it captures the strengths of specialized models and routes around their weaknesses, instead of settling for one model's compromises across the board. But this only works with a router that picks the model most likely to solve a given task, at the lowest cost, using the local evidence available before the rollout begins. Many of our work

On most enterprise tasks, model quality is not a scalar. One model is better at long-horizon repository exploration. Another is better at small, surgical patches. A third has stronger general reasoning but higher latency or cost. The right model depends on the task, the environment, and the failure mode that matters. This is especially apparent in agentic software engineering, where every task unfolds as a trajectory. The model reads files, searches the repository, forms a hypothesis, edits code, runs tests, and decides when to stop. The best model for one issue may be the wrong model for the

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