Are modern chess programs tactically perfect? In a given situation, can we assume that a chess engine is tactically perfect - meaning that it can find all possible tactics which are there in that position. Is it possible for the best of modern chess engines to miss a tactic in a given position? Has this ever been observed in any recent game?

EDIT: @Henry Keiter: For the purpose of this question, I would define tactic as a combination or sequence of moves which wins material(if playing the winning side), or a combination or sequence of moves which defends a difficult position successfully (meaning preventing a loss of material which seems imminent). I know that the general definition is broader but for the purpose of this question the meaning can be taken as defined above.

EDIT 2: The reason I am asking this question is I am actually curious if a chess engine really does find all the possible tactics in a given position, or if it just manages to find many more tactics than a human being would find.
The quality and depth of an engine's calculation depends on the


Performance of the machine the engine is running on
Quality of its algorithms in terms of calculation and performance
Time span allowed for calculating the position


It is generally considered that tactics (i.e. combinations that lead to winning material or delivering checkmate) are the engines' speciality. There is a double difficulty of calculating variations. First, to find the best sequence of moves for both sides. Second, to evaluate the final positions after the combination is completed. Todays state of the art engines are excellent at both. I would say that


  Todays best engines will not miss a single tactic.


Most probably, there are "tactical benchmark data sets" of positions that engines constantly are tested against. I don't recall any cases where engines missed tactics. But by going through the game databases, especially from engine versus engine tournaments, there should be examples of one engine calculating better than its opponent engine.