Why Your AI Agent Keeps Failing — Prompt vs Context vs Harness vs Loop

Your AI coding agent deletes a function that didn't need deleting. It tells you the bug is fixed — the tests are still failing. It gets stuck retrying the same broken edit forever. Most people's fix is the same every time: rewrite the prompt. But that's usually not where the problem lives. This video breaks down the four layers every AI agent actually runs on — Prompt, Context, Harness, and Loop — and gives you a simple diagnostic model for figuring out which layer actually broke before you touch anything else. You'll learn: What a language model actually is: a conditional predictor, not a rule-follower Why "the model saw it" and "you told it" are two completely different failure modes What a harness actually does between a model's decision and a real effect in the world The difference between the inner loop (one run) and the outer loop (whether it runs again) — and why a loop without an exit condition is dangerous A four-question diagnostic you can run on any failing agent The engineering trade-offs that show up as agents scale: cost, blast radius, verification, context rot If you build with AI agents, this is the mental model that replaces "just try a better prompt" with an actual systems-engineering approach. Next video: what happens when it's not one agent anymore — multiple agents working on the same codebase at once. Subscribe so you don't miss it. --- ⏱ Chapters 0:00 Chapter 1: Three Ways Agents Fail 1:07 Chapter 2: Meet the Four Rings 2:08 Chapter 3: Prompt Engineering 3:29 Chapter 4: Context Engineering 6:02 Chapter 5: Harness Engineering 8:42 Chapter 6: Loop Engineering 10:34 Chapter 7: Diagnostics 11:17 Chapter 8: Scaling & Trade-offs 14:33 Chapter 9: Putting It All Together --- 🔔 Subscribe for more on AI agents & system design: AI Under The Hood #AIAgents #SystemDesign #PromptEngineering #AIEngineering #LLM