The Bits-over-Random Metric compares your retrieval against random chance, which is how you catch the context pollution a high Success@K hides. Notes on staged tool routing and where the collapse regime starts.
A colleague shipped a custom assistant in a couple of hours by wiring Google Calendar, Microsoft To Do and Notion together. Execution stopped being the bottleneck, and here is a Gemini API scheduler you can start from inside WordPress.
My agent failed the same caching refactor three times before I fixed how I was briefing it. Planning mode, browser access through Playwright MCP, and claude.md files are what turned that into one-shot work.
A decade of explaining why WordPress sites don’t feel like apps, and what 2026 changes: blocks described to an AI instead of scaffolded by hand, the Interactivity API handling state without page reloads, and theme.json holding the design system.
My agents decided their own heartbeat config was redundant and deleted it, twice. The tiered OpenClaw setup that came out of it: orchestrators for judgment, throwaway personas for tasks, and five Markdown files that hold an agent’s identity.
Frontier models refuse a blunt request to cheat, then brute-force significance when the same request is dressed up as an upper-bound estimate. What that looks like in code, and why observational studies break first.
Buying every tool with an AI suffix is not a strategy. Splitting autonomous from augmented productivity, why a broken human process makes an agent hallucinate, and where the return on a 2026 AI budget actually sits.
An agent that is 85% accurate per step is about 20% accurate over ten steps. Lusser’s Law explains why, the Replit database deletion shows what it costs, and a gatekeeper pattern in PHP puts human review in front of the irreversible actions.
WordPress.com added MCP support, so an AI agent can read and write the site directly. The same update brings a site-wide newsletter default, a command palette shortcut, button state controls, UPI payments for India, and a fixed subscriber chart.
Autonomous agents fail in ways that are expensive to clean up. This is how LangGraph interrupts and checkpointers let a person approve a step mid-run, plus the idempotency mistake that made my content agent publish something I never signed off on.