Why agentic AI systems fail in WordPress production
Most AI pilots never make it past the demo. These are the five kinds of production debt that kill agentic AI systems in WordPress, from brittle prompt handling to the legal review nobody booked.
Tag: Agentic Commerce
LLM evaluation for WordPress AI agents, beyond vibe checks
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.
A pragmatic agentic AI workflow for WordPress
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.
WooCommerce AI workflows that go beyond static reports
The WooCommerce integration for Claude adds 18 prebuilt AI workflows that behave like a senior analyst inside your store, from revenue triage to inventory risk reviews. They run as slash commands over live data, not static charts.
The AI agent evaluation framework I use in production
Most AI agent projects fail because of weak evaluation, not weak models. These are the 12 metrics I run against every response, grouped into retrieval, generation, agent behavior, and production health, with PHP and Python examples.
When to scale AI to a multi-agent system
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.
AI agent security and the four attack surfaces
The four places an autonomous agent gets attacked (prompt, tools, memory, and the planning loop) and how to lock down a WordPress and PHP backend with least privilege, schema validation, and reasoning logs.
Agentic AI optimization and the end of guess-the-bottleneck
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.
Agentic commerce in WooCommerce: the latency problem
Beau Lebens talked about agentic commerce at Stripe Sessions, where AI agents do the shopping. My take on the hard parts: live inventory data, checkout security for bots, and what MCP and the Abilities API actually change.
Build an AI context layer so your LLM stops forgetting
RAG re-derives everything on every query, so nothing accumulates. This is the Raw and Wiki folder layout, the three control files, and the daily, weekly and monthly jobs I use to keep an AI context layer my agents can read.