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Tessl CLI

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Tessl Code Review: Catching 74% of Bugs Pre-Merge thumbnail

· 25:56

Tessl Code Review: Catching 74% of Bugs Pre-Merge

Tessl Code Review is an AI-native code reviewer designed to address the critical bottleneck of trusting code generated by coding agents in a software factory. It functions as an automated GitHub app that runs specialized, customizable review rules (lenses) against Pull Requests (PRs). The system emphasizes self-improvement loops, allowing the review process to learn from the codebase and the team's specific best practices, thereby increasing defect detection rates (claiming up to 74% detection).

Key takeaways

  1. AI Code Review and the Software Factory 5:56

    Building a 'software factory' requires more than just plumbing; it requires self-improvement loops that continuously refine the context and skills used by coding agents. Code review is positioned as a critical loop that feeds back into the factory to improve code quality before merging.

  2. Customization via Lenses and Globs 22:07

    The system uses customizable 'lenses' (review rules) that can be tuned to specific domains (e.g., audio DSP) and restricted to specific file paths using globs. This allows for highly targeted reviews, even in large mono-repos.

  3. Local and CI Integration 23:50

    Tessl Code Review can be run directly via the CLI (`Tessl code review give it the profile`) against local changes, enabling developers to catch defects before opening a PR, complementing its automated GitHub app integration.

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Agents Write 95% of Our Code. Here's the Catch thumbnail

· 29:43

Agents Write 95% of Our Code. Here's the Catch

As AI agents assume control over an estimated 95% of code production in advanced software factories, traditional code review processes are insufficient. The talk introduces the role of the 'harness engineer,' a new skill set focused on system-level controls: defining invariants, performing deep analytics on agent logs and PR data, and implementing fine-grained risk/operations policies (like auto-merge ladders). This shift requires engineers to move from writing code features to building robust guardrails that ensure consistency and quality across agent-driven pipelines.

Key takeaways

  1. The Paradox of AI Adoption 25:24

    While AI coding tool adoption is high, benchmarks are becoming saturated. Concurrently, the number of reported bugs and incidents is rising, indicating that agents may generate code that lacks maintainability or systemic health (00:15:24).

  2. The Rise of the Harness Engineer 9:34

    Engineering focus must shift from pure feature building to defining and enforcing system invariants. The three critical new skill sets are Systems Thinking, Analytics, and Risk/Operations (00:09:34).

  3. Instruction Following Gap in Skills 8:23

    Tessl's internal skills benchmark revealed that while agents achieved high task completion rates, they only followed approximately 70% of the total instructions defined within a skill (00:08:22).

  4. Systemic Control through Invariants and CI Gates 12:56

    Engineers must identify general principles (invariants)—such as design system rules or desired code structure—and encode them into deterministic checks, verifiers, or CI gates to ensure consistency across the codebase (00:12:56).

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