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All picks tagged “Code Review”.

Acquisition · 1Addiction · 1Aesthetics · 1Agent Architecture · 44Agent Engineering · 42Agent Environment · 1Agent Evaluation · 1Agent Infrastructure · 5Agent Loop · 1Agent-Memory · 3
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07-19

LangChain’s Open-Source Software Factory

LangChain open-sources four internal software engineering agent tools: local coding agent dcode, cloud coding agent OpenSWE, automated code review OpenSWE Review, and repo knowledge documentation OpenWiki. It includes real usage data (OpenSWE triggered ~1,000 times from Slack last week) and benchmark results (OpenSWE Review scores 47%, #1 among open-source tools). Built on Deep Agents framework with LangSmith observability. Targeted at engineers building controllable, observable agent pipelines.

x.com · 7 min · Agents · AI Engineering · Code Review
07-16

The Short Leash AI Coding Method For Beating Fable

This post distills over a year of research on using AI agents for security-critical software. The author introduces the “Short Leash” method: only expert developers can use it; never enable YOLO mode; manually review every diff in the permissions prompt to keep the AI on track; commit after each subtask to safeguard against regressions. It also details AI-assisted code review: pair human and AI, with AI catching surface errors and humans guiding direction. PR authors must self-review line-by-line and disclose AI models used. This approach beats Fable even with non‑frontier models, without sacrificing quality. Targeted at senior engineers who want productivity gains without giving up understanding.

blog.okturtles.org · 7 min · AI Agents · AI Engineering · Code
06-27

Agentic Code Review

When coding agents produce thousands of lines of often solid code in minutes, the engineering bottleneck shifts from writing to trusting, making review the most leveraged skill in software. Multi-source 2026 data (Faros AI, CodeRabbit, GitClear, GitHub) shows: AI users generate ~4x raw output but only ~12% more delivered value; code churn up 861%, defect rate from 9% to 54%, review duration up 441.5%, and zero-review merges up 31.3%. The article argues the fix is not to stop using AI but to tier review effort by blast radius: light for solo no-user projects, heavy for large enterprises. Specific advice: triage PRs upfront, require evidence before review, watch test rewrites, run two differently-structured AI reviewers in parallel, and upgrade humans from line-level review to spot-checking and auditing. The durable skill is understanding a system well enough to stand behind it.

addyosmani.com · 29 min · Agent Engineering · AI Engineering · Code Review