What is the best harness for AI coding?

Selecting an AI coding harness should focus on whether it can complete and verify real repository tasks with acceptable cost, time, and human intervention rather than on model names or benchmark scores alone. Factory argues that its Droid harness supports comparisons across models and describes features such as environment discovery, model-specific editing tools, planning, background-process tracking, and routing between models, while its vendor-published Terminal-Bench studies report higher pass rates and lower cost per passed task than several competing harnesses under specified configurations. These results are limited to the tested versions, models, terminal-focused tasks, permissions, and methodologies, so they cannot determine the best product for every workload or isolate the effect of any individual feature. The discussion recommends evaluating candidate systems on representative tasks while recording accepted changes, failed attempts, elapsed time, validation results, configuration differences, and required approvals. It also emphasizes that verification time, service cleanup, sandboxing, least-privilege access, and separation of benchmark permissions from production rollout policies are essential parts of assessing an agent’s practical reliability.

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