Recursive language models get a pointer to the data instead of having the whole thing pasted into the prompt. Why that beats ReAct and CodeAct on long-context work, and what it means for auditing a large WordPress codebase.
Mixing technical English tokens like run.py can push an AI coding assistant to drift in the embedding space, sometimes replying in Korean when prompted in Chinese. How task registers shape the behavior, and how to keep your prompts grounded.
Vibe checks do not scale. Here is how to score a WordPress AI agent on five measurable dimensions, accuracy, reliability, latency, cost, and business impact, using golden datasets and the LLM-as-a-judge pattern to move from fragile demos to production.
Instructions alone will not give a model an identity. Three ways to train one persona into a small model, and why first-person statements generalized better than chat demonstrations or factual documents.
How to move past vibe coding to a spec-driven agentic AI workflow for production WordPress: persistent specifications, guardrails against silent failures, and automated tests, using tools like CodeSpeak and the Claude API.
A practical workflow for Claude Code: use Plan Mode before it touches your code, keep a CLAUDE.md skill file so fixes stick, and keep the context lean. The goal is production-ready WordPress, not AI-generated technical debt.
Semantic search alone misses exact technical terms. Combining BM25 with dense vectors, then re-ranking the top results with a cross-encoder, is what made our production RAG accurate. RAGAS metrics show the difference.
Moving from a single AI agent to a multi-agent system pays off once workflows get complex. This covers the ReAct loop, specialized roles like retriever, writer, and verifier, and when to drop the monolithic prompt to cut hallucinations.
How I stopped re-explaining my projects to Claude every session. I keep a persistent knowledge base built from CLAUDE.md rules and a custom skill, and a nightly cron job keeps it current.
Manual performance tuning is mostly hypothesis and hope. Agentic loops like pi-autoresearch take a metric and a budget, run the experiments themselves, and keep only the versions that measure better.