Friendly engineering notes for teams building, evaluating, securing, and operating AI agents in real environments.
Opening
When engineers talk about AI agents, the conversation often jumps straight to the model: GPT, Claude, Gemini, Llama, Qwen, or another foundation model. That is understandable. The model is the most visible part of the system. It reasons, writes, summarizes, calls tools, and produces the answer we see.
But in production, the model is only one part of the operation.
The real engineering work sits around the model. That surrounding system is often called the agent harness. The harness controls how the model receives instructions, how it gets context, how it calls tools, how it handles errors, how humans approve actions, how logs are captured, and how the agent is evaluated after the task is complete.
