Instead of 'hill climbing' on public benchmarks like Terminal Bench, Factory focuses on solving difficult software problems from enterprise customers. This creates a proprietary dataset of realistic challenges that, when solved, naturally leads to strong performance on public benchmarks as a side effect.
By creating an AI 'skill' that synthesizes key company documents like product principles, value propositions, and frameworks, a product team can ensure that all generated outputs (e.g., PRDs) consistently reflect the company's specific language, strategic thinking, and established culture.
By performing a 'grounding step' where it reads an existing codebase's CSS, layouts, and components, an AI agent like Droid can build new features that automatically conform to the established design system. This eliminates the need for manual styling or explicit 'design system skills' to maintain visual consistency.
Use a highly intelligent model like Opus for high-level planning and a more diligent, execution-focused model like a GPT-Codex variant for implementation. This 'best of both worlds' approach within a model-agnostic harness leads to superior results compared to relying on a single model for all tasks.
Contrary to their name, software development agents are not just for coders. Their ability to interact with files, apps, and data makes them powerful productivity tools for non-technical roles like sales. This signals their evolution from niche coding assistants to general-purpose AI systems for any computer-based work.
An agent's effectiveness is limited by its ability to validate its own output. By building in rigorous, continuous validation—using linters, tests, and even visual QA via browser dev tools—the agent follows a 'measure twice, cut once' principle, leading to much higher quality results than agents that simply generate and iterate.
Droid's 'spec mode' asks users clarifying questions to define what to build, distinguishing it from 'plan mode' where users dictate implementation. This keeps the user focused on product requirements, letting the agent determine the optimal execution path, which is a more effective human-AI collaboration pattern.