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In this video, I define what an agent harness is (and what it isn’t), explain how it differs from frameworks like LangChain, LangGraph, AutoGen, and CrewAI, and show how a harness turns a one-shot model into an agent that can act, observe results, and iterate toward a solution. I break down nine key harness components: the while-loop engine, context management and compaction, tools vs. skills with a registry, subagent management, built-in skills, session persistence/memory, dynamic system prompt assembly from files like CLAUDE.md or AGENTS.md, lifecycle hooks (pre/post tool), and permissions/safety with dynamic command classification and user approvals. I then walk through a minimal Python reference implementation covering these pieces, including tool descriptors, subagent archetypes, append-only JSON event logs, and prompt assembly considerations like prefix caching.
My voice to text App: whryte.com
Website: https://engineerprompt.ai/
RAG Beyond Basics Course:
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00:00 What Is a Harness
01:50 Harness vs Frameworks
03:19 Nine Core Components
03:49 Loop and Context Control
05:41 Tools Skills and Subagents
06:57 Sponsor – Coursera
09:03 Memory Prompts and Hooks
11:41 Permissions and Safety Layer
13:04 Build a Mini Harness
13:18 Reference Implementation Walkthrough Read More Prompt Engineering
#AI #promptengineering
