Harness Engineering: The Part of Agentic AI Nobody Writes About
Everyone is tuning prompts and picking models. Almost nobody is talking about the harness. And after building agentic systems for enterprise customers over the last year, the harness is where most of my production problems actually live.
This isn't about prompt engineering. There's enough of that already. This is about the scaffolding around the model: how you assemble context, manage tools, handle retries, and catch what comes back. The model is the engine. The harness is everything that feeds it and everything that cleans up after it.
The short version
Your model is rarely the problem. How you feed it and how you handle its output is. Teams spend weeks tuning prompts when the real fix was upstream in the harness the whole time.
Context is the thing people underestimate
Everyone talks about prompts. Context matters more.
What I see with junior engineers is they keep tweaking the prompt when they don't get the answer they want. They'll ask the same thing five different ways. Most of the time the prompt isn't the problem. The model just didn't have enough context to work with.
That's why requirements, specs, business rules, instruction files, and knowledge bases matter more than the prompt does. The model can generate code all day. It can't know how your business works unless the harness puts that in front of it.
Where the harness breaks
Context bloat. You stuff in too much. Full history, every tool definition, whole documents. You burn tokens and the output gets worse, not better. More context is not better context.
Stale context. You feed the model a snapshot, the underlying data changes, and nobody re-feeds it. The harness has no sense of freshness, so the model is reasoning over a world that no longer exists.
Tool overload. Expose fifty tools and the model picks wrong. The tool definitions eat your context window before the real request even lands.
The mistake everyone makes
Teams treat the harness as plumbing. Boring code you write once and forget. So they pour all their effort into the prompt and the model choice, and leave the harness as an afterthought.
Then it breaks in production, and they go right back to tuning the prompt, because that's the part they've been taught to think about.
The prompt was never the problem.
What I'd build today
Treat context as a first-class design problem, not a string you concatenate. Decide what the model needs, when it needs it, and how it stays fresh. Keep the tool surface small and relevant per call. Log what you actually sent the model, not just what it sent back, so you can debug why it did what it did. And design for failure inside the loop, because a single tool call failing is easy. A multi-step run failing halfway is where real systems fall apart.
None of this is exciting. It's also where the leverage is.
Closing thought
We spent the last two years getting good at prompting. The next two are about getting good at the harness. The teams that win won't be the ones with the cleverest prompts. They'll be the ones who did the boring work of capturing their context and feeding it well.
The code still matters. But so do the specs, the instruction files, and the workflows that tell the model what it's supposed to do. That's where the real work is moving.
Karthik Karunanithi is a Solution Architect at IBM. Writing about what production agentic AI actually looks like in enterprise environments, including the parts that don't fit the marketing.
