This research has two facets within separate research areas. The research area of Belief, Desire and Intention (BDI) agent capability development was extended. Deception detection research has been advanced with the development of automation using BDI agents. BDI agents performed tasks automatically and autonomously. This study used these characteristics to automate deception detection with limited intervention of human users. This was a useful research area resulting in a capability general enough to have practical application by private individuals, investigators, organizations and others. The need for this research is grounded in the fact that humans are not very effective at detecting deception whether in written or spoken form. This research extends the deception detection capability research in that typical deception detection tools are labor intensive and require extraction of the text in question following ingestion into a deception detection tool. A neural network capability module was incorporated to lend the resulting prototype Machine Learning attributes. The prototype developed as a result of this research was able to classify online data as either "deceptive" or "not deceptive" with 85% accuracy. The false discovery rate for "deceptive" online data entries was 20% while the false discovery rate for "not deceptive" was 10%. The system showed stability during test runs. No computer crashes or other anomalous system behavior were observed during the testing phase. The prototype successfully interacted with an online data communications server database and processed data using Neural Network input vector generation algorithms within seconds. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]