WordPress 7.0 Beta 2 adds one Connectors screen for AI providers and forces Block API v3 blocks into the iframed editor. Both change what your plugin code has to do before the April 9 release.
Claude Skills keep instructions in files the agent reads only when needed, which cuts the token overhead of always-on MCP tools. Subagents push it further by throwing away their own context once the job is done.
AI-generated code ships fast and then stalls, usually around month three, when nobody can safely change the file it all lives in. A WooCommerce example shows the monolith version next to the decoupled one.
Classic RAG runs one retrieval pass and stays predictable. Agentic RAG keeps searching until it has enough evidence, and pays for it in latency and tokens. How I pick between them, and what breaks when a loop has no budget.
Demo agents fall over in production because errors compound across steps and prompts break when the business process changes. Structured output, LLM-as-a-judge evals and OKR alignment are what get a prototype across.
AI now generates faster than any of us can type, which makes judging the output the actual job. Where LLM code tends to break in WordPress: hooks that no longer exist, uncached get_option calls in loops, and raw SQL that HPOS will punish.
Most machine learning projects fail on framing, not on model quality. Five checks to run before you open a notebook: name the decision, price the two kinds of error, audit the target variable, simulate the deployment, and write down how the project fails while the metrics look fine.
WordPress 7.0’s AI updates are heading into Beta 2. This post covers the pivot away from the Abilities API, the new Connector Screen, and the dependency issues in the AI Experiments plugin.
February was short but it made a few things obvious: solo work stalls ML projects, code is not documentation, and an MLOps stack that ignores the deployment environment never ships. Notes from a month of ML and backend work.
Most AI features never leave the notebook. Notes on the production data architecture that gets them into a live site: separated layers, database integrity over raw insert speed, and the state and permission problems agents bring with them.