When dealing with the analysis of time and space complexity of algorithms, is it safe to assume that any function which has tight bounds ( i.e. $f(n)=\Theta(g(n))$ is asymptotically positive and asymptotically monotonically increasing. I mean that for all $n$ greater than or equal to some $n_0$ both those properties hold?
It is safe to assume that $f$ is everywhere positive, hence, asymptotically positive. You can't use negative time or space.
It is not safe to assume that $f$ is asymptotically monotonically increasing, since this excludes constant functions, i.e., those which are $\Theta(1)$.
It isn't even safe to assume that $f$ is asymptotically monotonically non-decreasing; this precludes functions that oscillate. A good question might be "do any useful algorithms have asymptotically oscillating time or space complexities," but certainly you could create an algorithm that did.
I suppose a more rigorous answer would ask what your definition of "asymptotically monotonically increasing" means. If it means that it's $\Theta(g(n))$ where $g(n)$ is monotonically increasing for positive $n$, then the answer would be yes, by definition.