SpaceXAI just released a free 33-minute course on building a complete Grok Bot harness from the ground up: 0% → 0:52 - understand the fundamentals of a Grok Bot harness 15% → 3:21 - create specialized agents for different tasks 30% → 5:42 - build an agent that tracks webinars and X 50% → 8:23 - put repetitive workflows on autopilot 65% → 11:43 - share agents across your team 80% → 18:15 - connect research and outreach agents 100% → 24:28 - turn an RFP into an estimate in 30 seconds instead of 2 hours Most people still rely on a single chatbot for every task. Harness engineering flips that approach. Instead of one AI doing everything, you build specialized agents with dedicated tools, workflows, and memory that work with minimal supervision. One company reportedly increased its pipeline by 140% in just one month using this approach. Watch the course to see the system in action and learn how to apply it to your own workflows.

A SpaceX AI engineer who helped ship Grok Bot discovered something important from her very first bug. “Oh, it has to be this.” The agent sounded completely certain. But its tool calls revealed the truth: it had never even opened the code it was blaming. Most people would call the model unreliable and return to checking every line manually. She took a different approach. Instead of blaming the model, she turned the missing context into a reusable skill: a simple Markdown file explaining how the agent should handle the task. Five months later: → 1,000 PRs in a single month → Agents reviewing and auto-merging their own work The process was simple: Prompt → Tool calls → Missing context → Skill → Trust The problem wasn't necessarily the model. It was the missing context that kept it from doing the job right. Bookmark this before blaming your next AI agent for getting your code wrong.