How AI Agents Work: Prompt, Context, Harness & Loop Explained
In a Nutshell
Prompt engineering frames better questions; context engineering supplies the right information. Harness engineering gives the model tools, guardrails, and feedback so it can act instead of just respond. Loop engineering removes the human trigger by building a system that repeatedly prompts, monitors, and verifies until a verifiable goal is met, provided the task repeats and has a crisp exit condition.
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The AI world loves buzzwords. New terms are popping up faster than most people can track, and it's getting hard to tell which ones actually mean something and which are just noise. That confusion is a problem. When everything sounds important, nothing does. But a few of these terms are worth knowing because they genuinely build on each other in a useful way: prompt engineering, context engineering, harness engineering, and loop engineering.
Prompt engineering is how to talk to AI. How you frame it matters as much as what you ask. A generic question like "What makes a Netflix show successful?" produces a paragraph about popular genres and viewership trends that could have been copied from a blog post, with no structure, no hypotheses, nothing actionable. This is a prompt engineering problem - the model was given no role, no specific question, no output format.
A better prompt: "You are a data analyst looking at Netflix content. What are three hypotheses for why TV shows might get renewed more often than movies? For each hypothesis, suggest one metric I could use to test it." This produces three structured hypotheses, each with a specific metric that can be tested. Role, specific question, output format - these three elements make the difference.
Prompt engineering works great for one-off questions, but the model still knows nothing about the actual project - column names, data quality issues, specific words of a CSV, or code. Every time something specific to the project is needed, the process starts from zero.
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