# Human vs. Agent Reliability Over Long Horizons – How Can We Do What They Can’t? > Source: > Published: 2026-09-19 03:58:16+00:00 [Multiple studies](https://codemanship.wordpress.com/2026/08/12/ai-software-development-what-does-the-data-say/) find that – without exception – the reliability of autonomous agent workflows decays geometrically over many steps. There are no examples of complex, *reliable* software created autonomously by agents – or anything even close. On a per step basis, humans are – of course – equally unreliable. And yet, somehow, there are many examples of complex, reliable software created by humans. Despite being just as fallible, humans are able to stabilise reliability in a way LLM-based agents can’t – despite all our best attempts to make them “self-correcting” or “self-healing”. I think the missing piece of the jigsaw lies in my simplified model of the [reliability of a step](https://codemanship.wordpress.com/2026/08/27/agentic-horizons-when-the-wheels-start-to-wobble/) in development workflows: R = 1 – (1 – C)(1 – P) Where C is the probability of it being correct, and P is the probability of any errors being caught before they propagate and compound – before the wheels start to wobble. I think what we’re looking for is in 1 – P. If P represents the distribution of errors that get caught, then 1 – P represents the errors that fall outside that distribution. These are the errors that weren’t *anticipated* – the tests the agent didn’t write, the rules the linter didn’t check, the things the model wasn’t trained on. The state of the art in self-correcting systems can only correct problems if they recognise them as problems. They can only self-correct if the problem is in P. It’s the gap between P and 1 that causes the instability in autonomous workflows, and there is *always* a gap. P = 1 is infinitely difficult to achieve on any non-trivial problem. Humans must have some capability that compensates – some ability to recognise and then adapt to the new and the unfamiliar, and with sparse data to go on. Cognitive science calls it [“learning”](https://arxiv.org/html/2603.15381v1).