Today’s business environment requires its workers to be skilled and knowledgeable in more than one area to compete. Data scientists are expected to be polyglots who understand math, code and can speak the language of business. This paper aims to develop a new Agent- Develop Kernel Gradient Boosting Machine (Agent-DKGBM) algorithm for prediction in huge and complex business databases. The Agent-DKGBM algorithm executes in two phases. In the first phase, building the cognitive agent, the primary goal is to prepare the database for the second phase, searching the business databases. During this phase, the cognitive agent selects one of the business databases, choosing the most suitable type i.e., Hyperbolic functions, Polynomial functions and Gaussian mixture as a kernel of Develop Support Vector Regression (DSVR) and determines the optimum parameters of the DSVR and DKGBM. The second phase consists of three stages, which include splitting the business databases into training and testing datasets by using 10-fold cross validation. In the second stage, a DKGBM model using the training data set is built to replace the Gradient Boosting Machine (GBM) kernel, typically using Decision Trees (DTs) to produce the predictor with DSVR because it would potentially increase the accuracy and reduce the execution time in the DKGBM model. Finally, the DKGBM would be verified based on the testing data set. Experimental results indicate that the proposed Agent-DKGBM algorithm will provide effective prediction with a significant high level of accuracy and compression ratio of execution time compared to other prediction techniques including CART, MARS, Random Forest, Tree Net, GBM and SVM. The results also reveal that by using Gaussian mixture as a kernel of DSVR, the Agent-DKGBM achieves more accurate and better prediction results than other kernel functions, which prediction algorithms typically use, also than GBM, which typically use DTs. Results clearly show that the proposed Agent-DKGBM improves the predictive accuracy, speed and cost of prediction. In addition, the results prove that Agent-DKGBM can serve as a promising choice for current prediction techniques.