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.
A fashion retailer updated a forecast on a Tuesday and the supply team saw it eleven days later, after the factory lead time had expired. Here is where that delay comes from and what integrated business planning changes about it.
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.
Plugging in the OpenAI API is not engineering. Notes on what actually matters when you build LLM features on WordPress: token costs, attention limits, RAG over fine-tuning, and keeping a 30-second API call from taking down PHP-FPM.
WordPress 7.0 brings real-time collaboration, and it asks a lot of the database and object cache. The Core team wants host test data by May 4th, so here is what the suite checks and where hosts usually get bitten.
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.
Discover how to enhance your website’s search engine performance using the power of schema markup. This comprehensive guide covers everything from basic microdata to advanced JSON-LD implementations. Learn why search engines love structured data and how you can use it to gain a competitive edge in search results starting right now for your business.
The data science job has moved. Production AI in 2026 is mostly orchestration: async APIs, vector databases and cost per request. Here is what changes when you start working as an AI architect instead.
LLM summarization pipelines invent facts when the schema demands a value the transcript cannot support. A three-stage design, extraction then synthesis then an audit stage that can only weaken claims, and why empty sections are correct.
Why scenario modelling is mostly about calibrated uncertainty rather than clean predictions, using an English local elections case study and a small PHP class for bootstrapping residuals.