Trendora

linting

Assess

Techniques

Automated code checks for syntax, style, and common errors.

Why it's here

Placed in Assess: 2 article(s) of evidence from 2 source(s), led by research-stage coverage, with 1 in the last 30 days. Confidence 45%.

Evidence (2)

  • 5The New Stack·7/30/2026research
    Linting is not enough for agentic development

    The article argues that AI agents increase development speed, but teams need broader verification than linting to ensure correctness, security, and reliability. It recommends combining local checks with control-flow and data-flow analysis, dependency and secret scanning, and workflow review to keep agent-generated code safe and maintainable.

  • 5Martin Fowler·4/2/2026research
    Harness engineering for coding agent users

    The article proposes a harness engineering mental model for coding agents to improve trust in AI-generated code. It argues for combining feedforward guides and feedback sensors, using both deterministic tooling and semantic evaluation to reduce errors before human review.