How AI Agents Work: Prompt, Context, Harness & Loop Explained

The AI world loves buzzwords – but few of these terms actually matter. Join Kristel Cocoli as she cuts through the noise and walks you through the four concepts that genuinely build on each other: prompt engineering, context engineering, harness engineering, and loop engineering. Using a Netflix titles dataset, you'll see each concept in action inside PyCharm: crafting a single well-structured prompt, feeding the model your project context, letting an agent read, execute, and fix code on its own, and designing self-triggering loops that keep your pipeline current while you work on something else. You'll learn how prompt engineering is about how you phrase things, how context engineering controls what the model sees, how harness engineering turns a model that talks into an agent that works, and how loop engineering builds the system that prompts your agent for you. Upgrade your workflow: https://www.jetbrains.com/ai/ ⏰ Timestamps: 00:00 – 00:40 Intro: cutting through the buzzwords 00:40 – 02:47 Prompt engineering 02:47 – 05:09 Context engineering 05:09 – 08:20 Harness engineering 08:20 – 10:33 Loop engineering 10:33 – 11:44 Caveats: token costs, vague goals, and guardrails 11:44 – 12:24 Outro 🔗 Useful links: ▶ JetBrains AI features: https://www.jetbrains.com/ai-ides/ ▶ JetBrains AI: https://www.jetbrains.com/ai/ ▶ ACP Registry: https://agentclientprotocol.com/get-started/registry ▶ Skills Support: https://www.jetbrains.com/help/ai-assistant/agent-skills.html ▶ Chroma on Context Rot: https://www.trychroma.com/research/context-rot ▶ LangChain blog on Context Engineering: https://www.langchain.com/blog/context-engineering-for-agents #jetbrains #ai #promptengineering #contextengineering #loopengineering #harness #aiagents #claude #codex #developerproductivity