Harness engineering

What happens when an AI agent is allowed to issue customer refunds? It may sound confident, follow the instructions, and still refund the wrong order. In this video, we use a simple customer-support refund assistant to understand harness engineering in practical terms. We explore how an AI agent can: Identify the correct customer and order Use safe, focused tools Follow a structured workflow Apply refund policies and guardrails Ask for human approval when needed Record tool calls and decisions through observability Recover safely from failures and timeouts Use evaluator agents to judge whether the work was actually correct We also look at the difference between a chatbot and an AI agent, and why a prompt alone is not enough for reliable real-world automation. The key idea is simple: The model is the brain. The harness provides the context, tools, memory, feedback, boundaries, and control that allow the agent to work safely. Chapters: 00:00 -The dangerous refund example 02:50 Chatbots versus AI agents 04:30 Why a naive agent can fail 05:35 Giving the agent reliable context 08:50 Designing safe tools 10:30 Observability and tracing agent behavior 12:40 Guardrails and human approval 14:30 Observability and tracing 17:50 Evaluators, testing, and feedback loops 19:45 Memory, retries, and recovery 22:40 Final takeaway #HarnessEngineering #AIAgents #AgenticAI #Observability #AIEngineering #LLM