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.
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.
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.
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.
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.
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.
WooCommerce for Claude came out of Automattic’s Radical Speed Month. It uses MCP to give the model analytics lookup tables, a store knowledge layer and an AI-readiness score, all on one endpoint. Plus the provider pattern and how to run it locally.
Ghost is an agent-first Postgres platform. Notes from using its MCP server and database forking to let AI agents test schema changes and index strategies without touching a shared staging database.
Prompt caching only pays off when the prefix stays byte-identical. I go through the mistakes that break it, a semantic cache layer for WordPress, loading tool definitions on demand, and what to keep or drop between turns.
A logistics director with all-green KPIs and 18% late shipments does not have a data problem. Scoped AI agents watching the TMS can tell you which handoff actually broke, and tell you on the morning it happens.