Which AI coding agent is best for your team?

Choosing an AI coding agent should be based on realistic, well-defined engineering tasks and a consistent trial setup rather than product demonstrations, interface assumptions, or benchmark rankings alone. The comparison should evaluate each vendor’s documented workflows, deployment requirements, model options, repository integrations, execution controls, and ability to complete implementation, validation, and handoff work, while using the same repository revision, instructions, and task boundaries for all candidates. Factory’s reported benchmark results provide evidence for specific tested configurations but are not universal rankings, and teams should avoid combining incompatible metrics such as task resolution, review accuracy, and cost into a single score without a sound methodology. Evaluations should record human interventions, elapsed time versus active supervision, environment repairs, validation results, unresolved issues, and abandoned attempts to reveal the true operational effort involved. Model flexibility can support organizational requirements, but changes in models or routing should be documented and should not weaken repository policies, approval processes, or independent checks. A practical adoption decision identifies specific task classes and configurations where the agent has proven useful, preserves trial cases for future retesting, and maintains clear limits for higher-risk work such as architecture changes or sensitive migrations.

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