#agents

Automation agents most need stable execution and deterministic output, and OpenClaw falls short on both: the same task gives different results, failures go unreported, and success cannot be guaranteed. Our fix is to add inspection logs and failure notifications, script the processing steps, templatize prompts, and make outputs JSON with strict validation.

Intermediate tutorial: copy the prompt from this site's BlenderMCP Skill page and give it to your Agent, which installs everything for you; after a restart, open Blender and model, change materials, and arrange scenes one sentence at a time, then save and run a pre-delivery check. Suited to 3D prototypes, asset sketches, and scene layouts.

Edit the text and numbers on a ready-made motion template, re-render, and download the finished video, with a consistent style.

Intermediate tutorial: No more opening multiple terminals and manually shuttling messages between them. One command, npx tmux-bridge-mcp setup, lets Claude Code, Codex, Gemini CLI, and Kimi Code read each other's terminals, send each other tasks, and hand work off in relay, all through MCP. Includes concrete steps for split panes, naming, and collaboration.

Beginner tutorial: Create a long-lived access token in Home Assistant, hand a short prompt to your AI agent, and it can check the status of your home devices and, with your approval, turn lights on and off for you.

Every major model provider now lets a model call functions you define. What that means for a workflow you run at work, and the one setting to check in your tool.

A report of 600 to 1,200 words with a claims table: each claim, the sources that support it, the sources that contradict it, and a confidence note. Plus a run log of every search and read.

A plain-language definition you can use in a meeting, the three parts every Agent has, and how to tell a real one from a chatbot with a nicer name.