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.
Claude Code forgets everything when the session ends. The setup I use: a post-mortem command that writes to CLAUDE.md, skill files for the quirks of the stack, and a daily pass over the logs.
Agentic loops fail quietly: the same query retried with new wording, retries with no circuit breaker, a context window full of raw JSON. Three patterns, how to measure them, and a PHP budget gatekeeper.
Offline evaluation for LLM agents: routing accuracy, LLM-as-judge scoring, and RAG faithfulness, plus the quality gate I use to block a deploy when the scores come in under threshold.
The API bill is the usual reason teams look at their own hardware, and privacy is the better one. Which benchmarks to trust for agents, how far you can quantize before logic breaks, what a single A100 on GCP costs, and serving Qwen 3.5-27B with vLLM.
Vector search handles synonyms well and loses exact identifiers. Notes on pairing BM25 with vector retrieval, merging both with rank fusion, and letting the agent retry when the results come back empty.
Chaining agents multiplies errors instead of dividing work: DeepMind measured up to 17.2x amplification. Here is the math behind that, the three topologies that hold up in production, and the checklist I run before shipping an agentic system.
Dashboards are no longer the main consumer of your data. Here is what I would change in a data stack to make it readable by agents: fewer tools, open table formats, a real semantic layer, and feedback loops that act without waiting for a human.
A tool is one capability, a skill is the orchestration around it. Notes on writing agent skills in Python instead of Markdown, and where MCP fits when you build agents outside Claude.