Using Linters to Direct Agents (www.factory.ai)

The article highlights a transformative shift in software development where AI agents—not just developers—are directly generating code, necessitating stricter, machine-enforceable rules to guide them. Traditional human-driven guardrails like manual code reviews and style conventions are proving insufficient. Instead, linters are evolving from mere style advisors into authoritative “laws” embedded in the development loop, encoding architectural standards, security policies, and testing discipline that agents must follow. This integration enables agents to self-correct and generate consistent, scalable code with minimal human intervention, fundamentally changing the dynamics of collaboration into a “compiler-like” interaction. Technically, agent-focused linters enforce a variety of critical constraints: consistent naming and formatting (grep-ability), predictable file structure (glob-ability), strict architectural boundaries, security measures to block secrets and unsafe patterns, test coverage rules, observability standards, and documentation requirements. These lint rules translate human architectural intents from prose (AGENTS.md guidelines) into executable, automatic checks with clear severity, autofixes, and waivers. This guarantees agent outputs match organizational standards, transforming lint passing into a proxy for “done.” Furthermore, linters act as a continuous migration engine, safely automating large-scale refactors and upgrades by encoding legacy patterns as errors and new standards as autofixes, streamlining long-term code health. By converting human conventions into automated, enforceable constraints, this approach addresses the new bottleneck in AI-assisted development: human feedback speed. It empowers agents to operate autonomously and reliably, reducing review overhead and technical debt while increasing codebase consistency and maintainability. This paradigm exemplifies how tightly integrated tooling bridges human intent and AI execution, accelerating agent-native development from proof-of-concept to robust, scalable engineering practice.