It's very difficult to provide a useful answer to this question because you haven't provided enough details about your problem and what you have already tried.
There are various conditions under which the augmented Lagrangian method is certain to converge asymptotically to an optimal solution. You should make sure that the problem you're trying to solve satisfies these conditions so that there's a sound theoretical basis for what you're doing.
In the augmented Lagrangian method, you solve an unconstrained minimization problem within each major iteration. If the procedure that you're using to solve that unconstrained problem isn't working properly than the overall method can easily fail to converge to a solution.
The augmented Lagrangian method makes use of a penalty parameter that can be adjusted after each major iteration of the algorithm. In theory, the method is guaranteed to converge if a large enough penalty parameter is used. In practice, using too large a penalty parameter can make it hard to solve the unconstrained minimization problem and slow down convergence.
Starting with a bad value of the parameter (too big or too small) or adjusting the parameter incorrectly (not increasing the penalty enough) can lead to slow convergence or complete failure. In my experience, it's almost always necessary to run some experiments to adjust the penalty parameter for good convergence on a new class of problems.
You haven't said whether the method is failing entirely or simply converging slowly on your problem. For users who are used to the fast quadratic convergence of Newton based methods, it can be frustrating to see the slower convergence of augmented Lagrangian methods. You're often limited in practice to getting solutions that are only accurate to a few digits. If you need solutions that are accurate to 8-10 digits, than augmented Lagrangian methods are probably not the best way to go.
My recommendations for debugging this would be:
Make sure you've got a theoretical convergence result for the class of problems you're considering.
Make sure that your code is correctly minimizing the augmented Lagrangian in each iteration. You may be able to get away with an imprecise solution later, but for debugging I'd just try for the best accuracy you can get.
Adjust the penalty parameter so that you're getting reasonably fast convergence to feasibility.
You should obviously be monitoring the feasibility of the solution after each major iteration. Assuming the constraints are reasonably scaled, simply computing the norm of the constraint violations is usually a good measure. You can also track the evolution of the Lagrange multipliers to see whether they're converging to some fixed values.
If you've got access to some other reliable solver, you can solve your problem using that solver first so that you know what an optimal solution looks like.
