AI Agents Are Writing Code Faster Than Anyone Can Review It. Tessl Is Betting On Standards To Close That Gap

Every team that has implemented coding agents has encountered the same issue: the AI-generated code is written quickly and in large quantities, but the human reviewers quickly fall behind. This is a real problem that Tessl is attempting to resolve with the introduction of Tessl Code Review, a free tool during its beta phase. The tool's main selling point is that it operates independently, filters out unnecessary issues, and understands the team's standards.

Tessl Code Review evaluates pull requests against these standards, which are stored as versioned files in the repository. This approach allows teams to customize the review standards and move them to another tool if they decide to switch. Unlike other AI code review tools that embed review logic within the product, Tessl separates the standards from the reviewer, making it easier for teams to manage and control the process.

The tool reads the entire pull request, including the surrounding codebase, team standards, and previous discussions, providing traceable feedback instead of generic comments based on changed lines. This contextual review results in fewer repeated issues and a more efficient review process. Mitch Ashley, an analyst from The Futurum Group, noted that agent-written code often stalls at the review stage, causing review quality to suffer.

He emphasized the importance of having visible, versioned standards owned by the team, as reviewers cannot enforce what they cannot see. The rapid growth of agentic coding tools is creating a bottleneck in the review process, and simply adding more human reviewers is not a sustainable solution. Tessl's emphasis on ownership, with standards living in the repo and being versioned, sets it apart from competitors and offers a lower cost alternative for teams to evaluate the effectiveness of whole-PR context and standards-as-code in their review process.