# Software agents

Recording real graphical-interface operations, parameterizing them into a frozen workflow and handing that to an agent can cover more business requirements than letting the agent write code directly. On three complete-task instances of one SaaS business family, the frozen workflow reached 33/33 local core points while raw code reached 8, 8 and 18; on two prospectively fixed instances the tool agents finished in six and five model calls, against 121 and 70 for raw code. Every attempt still failed whole-task acceptance, and collateral state preservation remains unknown.

Recording real graphical-interface operations, parameterizing them into a frozen workflow and handing that to an agent can cover more business requirements than letting the agent write code directly. On three complete-task instances of one SaaS business family, the frozen workflow reached 33/33 local core points while raw code reached 8, 8 and 18; on two prospectively fixed instances the tool agents finished in six and five model calls, against 121 and 70 for raw code. Every attempt still failed whole-task acceptance, and collateral state preservation remains unknown.

Reinforcement learning rewards for software engineering agents no longer stop at pass or fail: the current policy compares debugging traces from the same task and verifier outcome, turning those preferences into bounded auxiliary rewards and lifting Qwen3-14B average accuracy on SWE-bench Verified from 32.1% to 34.7%.

Reinforcement learning rewards for software engineering agents no longer stop at pass or fail: the current policy compares debugging traces from the same task and verifier outcome, turning those preferences into bounded auxiliary rewards and lifting Qwen3-14B average accuracy on SWE-bench Verified from 32.1% to 34.7%.