Step 1: Use the given condition \( M^2 = M + 2I \).
The matrix equation \( M^2 = M + 2I \) suggests that the eigenvalues of \( M \) satisfy the equation:
\[
\lambda^2 = \lambda + 2,
\]
where \( \lambda \) represents the eigenvalue of \( M \).
Step 2: Solve for the eigenvalues.
Rearranging the equation:
\[
\lambda^2 - \lambda - 2 = 0.
\]
Factoring the quadratic equation:
\[
(\lambda - 2)(\lambda + 1) = 0.
\]
Thus, the eigenvalues of \( M \) are \( \lambda = 2 \) and \( \lambda = -1 \).
Step 3: Use the relationship between the eigenvalues.
We are given that \( \alpha \beta \gamma = -4 \), and since there are three eigenvalues for \( M \), we conclude that:
\[
\alpha = 2, \quad \beta = -1, \quad \gamma = -2.
\]
Step 4: Compute the sum of the eigenvalues.
Thus, the sum of the eigenvalues is:
\[
\alpha + \beta + \gamma = 2 + (-1) + (-2) = -1.
\]
Step 5: Conclusion.
Therefore, \( \alpha + \beta + \gamma \) is:
\[
\boxed{-1}.
\]