Ok, read it: I read articles like this so you do not have to. This one is long, has many diagrams and a new word: Harness Engineering. HackerNoon itself attaches two labels to it, opinion and AI-assisted. You can tell. It is not wrong. It is just eighty percent familiar engineering craft with new names. And twenty percent a point that is right.
What it says
The travel talk reduced to the minimum: the agent gets a task. It gets context. It makes a plan. It uses defined capabilities, not raw scripts. It executes. The result is checked. A human steps in when it is risky. Everything is logged.
In the article that is Intent, Context, Plan, Skill, Execute, Validate, Review, Feedback. And above it sits an architecture in five layers, from business intent at the top to the runtime at the bottom, with the "harness" as the control layer in between. Plus seven gates before anything may go to production, three kinds of evidence and a tier model for risk: small runs automatically, big needs a signature.
What is right about it
Two things, and they are important enough that I do not throw the article away because of them.
The model is not the system. The agent should not call the tools directly. In between sits a layer with permissions, context, checks, observation and undo. That is exactly the rule I have been repeating here for a year: the AI proposes, the software decides. And it is the reason I wrote in the MCP article that the tools belong in scripts, not in the model's context.
A skill is not a prompt. A prompt is an instruction. A skill has a defined input, context, rules, execution, validation, undo and output. Whoever does not separate those gets, instead of "humans write throwaway scripts", only "the AI writes throwaway scripts". That sentence is good. I would have liked to write it myself.
The question that is missing
The article answers one question: how do we control agents? That is the right question for today. But it stops where it gets interesting for me. Its feedback is logs, metrics, errors and approvals going back to the agent. A circle. Every run starts again at zero.
What it does not ask: what do we learn from the sequence? A project really looks like this: first prompt, plan A, code A, test red. Prompt changed, plan B, code B, test half green. The agent gets an additional source, the context changes, plan C. A human corrects an architecture decision, the work package changes. Build green. That chain is worth more than any single run. It shows how a project converges. And whoever records it has a training dataset that exists nowhere else.
What stays
Read the article if you have to explain to someone why an agent must not go straight to the database. The diagrams for that are usable. Skip the parts about Snowflake and Databricks, that is market watching. And when you hear the words Harness Engineering in a meeting, you now know what is meant: engineering craft. The thing you already know.
Eighty percent new names for old mechanisms. Twenty percent that are right: the model is not the system, and a skill is not a prompt. And one question that is missing: agents do not need another harness. They need a memory.
Source: Nie Lifeng, "Rebuilding Data Engineering with Harness Engineering: A New Paradigm for the Agent Era", HackerNoon, 27 September 2026. Labelled by HackerNoon as opinion and AI-assisted. hackernoon.com
Written by me. The thoughts, the values, the learnings, the mistakes: all mine. Grammar and spelling are corrected by a twin model of my own, trained on my texts. Sometimes that turns into a mix. Of course I read everything again, but nobody is infallible. Whatever stays in is mine too.