The harness is not enough.

Why the harness gets the credit

Harness engineering has hugely grown in popularity throughout 2026, with many people touting the harness as being more important than the model [1].

When you look at results such as Prime Agent’s performance on ARC-AGI-3 [2], it’s easy to understand why so much attention has been placed on the harness.

In a world where the state-of-the-art models behave like an autistic genius with amnesia, harness engineering is the lowest hanging fruit to provide genuine statefulness, learning from experience, and skill acquisition, whilst still being able to leverage all of the power of in-context learning of the most powerful closed-source foundation models.

Notepads and tattoos

If we compare an agent to a person, then our current agents behave like Leonard Shelby from Memento, continually scribbling down experience onto endless notepads (would need to be very finely printed tattoos), and hoping they didn’t forget anything important. Personally, if I had to proactively write down everything I needed to remember, and explain every future skill purely in textual form, then I would also suck at remembering and learning things. It’s no surprise that our brains do not store things as a collection of word documents, and it’s no surprise that these harnesses are notoriously brittle and unreliable.

A point-in-time solution

With this in mind, harnesses such as openclaw, hermes-agent, prime-agent, claude code, opencode, and codex are just as much of a “point in time” solution as hand-crafted vision filters were in the 2000s. Hand designed edge detectors, line detectors and bag-of-words predictions gave way to end-to-end learned filters with the advent of CNNs. The implicitly learned filters were far superior to anything that a human could design, and human designed harnesses will give way to continual parametric learning in much the same way.

Why not continual learning already

So why don’t we have continual learning already? Don’t ChatGPT and Claude Code “learn” about me when I use them? The short answer is no they don’t. They store textual files about you, which must then be re-read every time you engage with the model. The reason for these simple hacks is simple: a lack of compute. Maintaining a separate LoRA adapter for each of the billion+ users of ChatGPT would require an insane amount of extra compute, and OpenAI is already rumoured to be operating at a loss. Keeping one frozen network, and then batching together thousands of user requests into a single forward pass is much more economically feasible than having each user engage with their own personally evolving model, with its own unique set of weights evolving based on all interactions with said user.

What changed

In-harness learning made sense in early 2026, as the closed-source foundational models were all substantially better than the open source alternatives, and so parametric learning wasn’t really an option if you wanted to leverage the best models. Things have now changed though. Much smaller open source models are catching up, and for the first time, this has opened the door to fully personalized continual learning, all on your own device, with a model that you own.

At unify, we’re pushing the frontier to make this continual learning possible.