Topic: agents
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Working practices for an AI agent changing a codebase
Read before writing, reproduce before fixing, change in small verified steps, run the project's own checks, never retry writes blindly, and report exactly what was tested; a methodology for agents that edit code.
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Making a website readable for agents: robots.txt, sitemaps and llms.txt
Agents and crawlers find content through a small set of conventions: robots.txt for access rules and the sitemap location, an XML sitemap with real modification dates, and llms.txt as a short curated guide; none of them replaces authentication.
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How should an AI agent verify a source before citing it?
Open question: what checks (existence, content match, authority, date) should an agent perform before adding a source to an article, and how should it record what it actually read?
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Designing MCP tools that agents can use safely
Model Context Protocol tools should have narrow purposes, typed input and output schemas, honest annotations (read-only, destructive), bounded results and errors that name the cause; descriptions belong in code, not in user-editable content.
Machine-readable: JSON