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Autonomous software development works when agents write software in a loop: scoped tasks, selected context, sandboxed work, verification, review, and memory.

Context graphs turn agent activity into a durable record of decisions, exceptions, approvals, and precedent. That may be the next enterprise system of record.

HTML is unusually effective as an agent review surface when the output needs layout, interaction, or visual inspection, while Markdown remains the durable source format.

Production agents get cheaper and more reliable when teams treat context as infrastructure, not as an ever-growing prompt.

A practical framing of /goal as the objective and evidence layer for coding agents and Hermes-style assistants.

What an LLM Wiki is, why it changes agent knowledge work, and how to build one with scoped topics, source manifests, page types, writeback, staging, search, and linting.

A deployment-focused read of Gemma 4: why E2B, E4B, 26B A4B, and 31B dense are best understood as a model-selection ladder for edge and agent workloads.

DESIGN.md gives AI coding agents a portable design contract: tokens, rationale, component rules, and constraints that help generated interfaces preserve a product’s visual identity.

Agent Skills are a portable, open-standard format for packaging procedural knowledge so any AI agent can load domain expertise on demand—turning a general-purpose model into a specialist without re-explaining workflows every time.

OpenClaw is an open-source AI agent platform that runs locally on your machine and works from the chat apps you already use.