We study the exit time from a bounded multi-dimensional domain $Ω$ of the stochastic process $\mathbf{Y}_\varepsilon=\mathbf{Y}_\varepsilon(t,a)$, $t\geqslant 0$, $a\in \mathcal{A}$, governed by the overdamped Langevin dynamics \begin{equation*} d\mathbf{Y}_\varepsilon =-\nabla V(\mathbf{Y}_\varepsilon) dt +\sqrt{2}\varepsilon\, d\mathbf{W}, \qquad \mathbf{Y}_\varepsilon(0,a)\equiv x\inΩ\end{equation*} where $\varepsilon$ is a small positive parameter, $\mathcal{A}$ is a sample space, $\mathbf{W}$ is a $n$-dimensional Wiener process. The exit time corresponds to the first hitting of $\partialΩ$ by the trajectories of the above dynamical system and the expectation value of this exit time solves the boundary value problem \begin{equation*} (-\varepsilon^2Δ+\nabla V\cdot \nabla)u_\varepsilon=1\quad\text{in}\quadΩ,\qquad u_\varepsilon=0\quad\text{on}\quad\partialΩ. \end{equation*} We assume that the function $V$ is smooth enough and has the only minimum at the origin (contained in $Ω$); the minimum can be degenerate. At other points of $Ω$, the gradient of $V$ is non-zero and the normal derivative of $V$ at the boundary $\partialΩ$ does not vanish as well. Our main result is a complete asymptotic expansion for $u_\varepsilon$ as well as for the lowest eigenvalue of the considered problem and for the associated eigenfunction. The asymptotics for $u_\varepsilon$ involves a term exponentially large $\varepsilon$; we find this term in a closed form. Apart of this term, we also construct a power in $\varepsilon$ asymptotic expansion such that this expansion and a mentioned exponentially large term approximate $u_\varepsilon$ up to arbitrarily power of $\varepsilon$. We also discuss some probabilistic aspects of our results.