Doesn't that mean we should expect that Bayesians often disagree and they have no way to resolve it except consulting reality (i.e., an experiment)?

Short answer: Yes.

Longer answer: Two Bayesians who start out with the same prior probabilities, and see the same evidence, should update their posterior probabilities in the same way, and so their mental models should stay consistent with each other. Two Bayesians who start out with different prior probabilities, but see the same evidence, should update their posterior probabilities in ways that are predictable to each other, and in line with the evidence - that is, if one reasoner (A)'s prior probability that (for example) General Relativity is true was high, while another (B)'s was low, then when an experiment is run which provides evidence for general relativity, A's estimates of General Relativity's likelihood of being true will change less than B's (because B's priors were more wrong), but both will update in a direction and to an extent that is predictable to either of them. As they see more and more of the same evidence, their models of the world should converge.

This is all assuming an ideal Bayesian reasoner with practically-unlimited computing power who doesn't cheat or decide not to reason according to Bayesian rules when it becomes inconvenient, and humans don't meet those constraints. But, there's math to say how much you should update given particular evidence. So:

Also say there is an experiment, is there any standard or agreement among bayesians about how to weight credence?

Yep. "How to weight credence" is a bit unclearly stated, but there's Bayes' formula, which tells you how to update your probabilities based on evidence, and that might be what you're getting at?

Which is (one reason) why bother with Bayesianism at all. It's a method of approaching consensus when working under uncertainty. It's kind of an "agreeing to the rules of the game" situation, where "the rules" are a mathematical equation that says how probabilities must change when people are disagreeing (and "must" here carries the same level of mathematical strength as saying "2+2 must equal 4", it's not a thing that was decided by committee) - if for example you say it's 95% unlikely/5% likely that something will happen under your idea of how the world works, and then it happens, if you're playing fair, you make a big update, and if you put numbers on it, Bayes' rule tells you what your new numbers should be. If you don't like the new numbers, you have to either acknowledge that what you said your priors were was incorrect, or that what you said your likelihood estimates of different outcomes were was incorrect - so either you retroactively revise how you used to think the world works, or you retroactively revise what you thought would happen and how confident you were, both of which are kind of awkward and embarrassing. And the people you're disagreeing with, if you don't make an appropriately sized update given what you told them your priors and likelihood estimates were, can point this out as a fact. And if you're not very confident, or you don't think particular evidence should carry much weight, you can express that in a way that makes it clear how much you're going to update based on whatever evidence you see, before you see it, so it isn't like:

"I think x is definitely wrong, and y will provide strong evidence"
"OK, so y didn't go how I thought, now I think x is only almost certain to be false"

It's like:

"I think X is a% likely to be false, and Y experiment will turn out the way I expect with b% likelihood as a result."

"Oh. Ok, well, I guess now I'm down to c% likelihood that X is false. Shoot."

And instead of being like "it's not fair that you moved from definitely to almost certain based on something you said would provide strong evidence but didn't go the way you expected", the reaction is "yep, that math is correct, you updated how you should have given your priors". And before you get to that point, pre-experiement, you can argue over whether b% likelihood is reasonable, where it's hard to argue about the correct meaning of the word "strong".

And once you get really familiar with doing this (I'm still not great at it), you know intuitively how much putting X% probability on a particular outcome means you're going to have to change your views if it doesn't happen, and you become appropriately cautious ("calibrated") in your estimates, and your saying things in probabilities conveys a lot of information to other people who are also familiar with talking this way.

All of that is in idealized theory among people who are quite smart and can do lots of calculation in their heads. Lots of people also LARP it and use Bayesian-sounding words without actually having the deeper intuitive understanding of what what they're saying means.