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Currently Playing: Prompt vs Context vs Harness vs Loop Engineering | Build Production-Ready AI Agents

Prompt Engineering is only the first layer of building modern AI agents. In this beginner-friendly tutorial, we compare: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering using one practical AI coding-agent example: GitHub Issue → Inspect Code → Fix → Test → Retry → Prepare PR You’ll learn: Prompt Engineering — how we control instructions, roles, constraints, examples, and output Context Engineering — how we provide the right code, memory, logs, RAG results, and tool outputs Harness Engineering — how tools, APIs, permissions, sandboxing, memory, validation, and guardrails surround the LLM Loop Engineering — how agents plan, act, observe, verify, retry, and stop safely How all four layers combine to create a production-ready AI agent Important controls such as IAM, human approval, observability, validation, cost limits, and max iterations Common production mistakes and interview-ready architecture concepts ### Simple Mental Model Prompt = Instructions Context = Information Harness = Capabilities Loop = Iteration Together: Production AI Agent = LLM + Context + Tools + Memory + Harness + Loop + Governance This video is useful for: AI Engineers, GenAI Engineers, AI/ML Architects, Agentic AI Developers, Software Engineers, Solution Architects, and anyone preparing for AI architecture interviews. Subscribe to Naveen TechHub for practical tutorials on Generative AI, Agentic AI, Cloud, DevOps, Data Engineering, and System Design. #PromptEngineering #ContextEngineering #HarnessEngineering #LoopEngineering #AgenticAI #AIAgents #GenerativeAI #LLM #AIEngineering #GenAI #RAG #AIArchitecture #SoftwareEngineering #NaveenTechHub