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There is a widespread increase of adoption of e-learning platforms, there is a great need to develop effective methods for assessing students’ cognitive abilities to deal with that environments. Traditional assessment methods need to improve in terms of analyzing the diverse range of skills and knowledge that students acquire during online learning. The current state of research work considers different levels of Bloom’s Taxonomy, which are used to evaluate students’ performance based on given text analysis. For this purpose, the Software Engineering course domain is considered. An online test was conducted among graduate university level students, consisting of 12 subjective-type questions, where 2 questions chosen to be tested at each level, concluded as 12 questions per student at accumulated levels of taxonomy. There exists Approx. 300 students’, who attempted the test and hence their textual responses are being used to evaluate the system. The methodology deploys the SVM classifier to predict the level of Bloom’s Taxonomy for exam questions and then, multi-agent system is developed to match and identify each level assigned to an agent. Each instruction based agent is trained on Random Forest Classifier to evaluate students’ cognitive skill on a scale of outcome as Good, Bad and Average. Tools, techniques, technology missing. The outcomes show that the proposed model performs much better than the existing models, provided with 98% accuracy for SVM Classifier and 92% accuracy for Random Forest Classifier to assess students’ textual responses. The novelty of this research work spins around agent prediction of cognitive level for assessment by a use of multi-agent system to recognize students’ strengths and weakness as per six levels of Bloom’s Taxonomy.