We love saying "our AI fixed the bug" or "our AI searched Gmail." But let's be honest—our LLM just talked about it. The real work happened in a layer we barely notice: the harness. It's the unsung infrastructure that gives our model a workplace, tools, memory, and guardrails (so it doesn't accidentally delete production). In this post, we pull back the curtain on the invisible machinery that turns a glorified text predictor into an agent that can actually act—and why the harness matters every bit as much as the model.
New to Claude, LLMs, agents, MCP, or the concepts behind CCAR-F? This foundations path covers the essential building blocks we need before diving into the five CCAR-F exam domains. Simple explanations, practical examples, illustrations, and hopefully without turning a few basic questions into another 27 browser tabs.
I'm preparing for CCAR-F Domain 1: Agentic Architecture & Orchestration, the largest domain at 27% of the exam. We'll break down what Anthropic expects us to understand about agentic loops, multi-agent orchestration, subagents, task decomposition, workflows, Agent SDK hooks, and session management — in plain English, with examples, illustrations, hands-on experiments, and the architectural trade-offs that actually matter.
