{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/157177"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/157177","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A Study on Deploying Large Language Models as Agents","abstract":"This thesis investigates the deployment and utilization of Large Language Models (LLMs) as agents, exploring their potential in automating workflows and enhancing user interactions. The study begins with an in-depth analysis of language models, tracing their evolution from pure statistical models to advanced neural network architectures like Transformers and their bidirectional variants. It then delves into the operational framework of LLM agents, detailing user interactions, environmental considerations, memory management, task planning, and tool use. The study addresses critical limitations in LLM inputs, such as the context window and introduces Retrieval-Augmented Generation (RAG) as a solution to extend the model’s capability. Key APIs provided by OpenAI for deploying GPT models are discussed, highlighting their functionalities and applications. Finally, the practical application of LLMs in creating Robotic Process Automation (RPA) workflows is demonstrated through a divide-and-conquer methodology, showcasing the efficiency, scalability, flexibility, and accuracy of this approach. This comprehensive study underscores the transformative impact of LLMs in automating complex processes and enhancing user experiences through intelligent agent deployment.","abstract_html":"This thesis investigates the deployment and utilization of Large Language Models (LLMs) as agents, exploring their potential in automating workflows and enhancing user interactions. The study begins with an in-depth analysis of language models, tracing their evolution from pure statistical models to advanced neural network architectures like Transformers and their bidirectional variants. It then delves into the operational framework of LLM agents, detailing user interactions, environmental considerations, memory management, task planning, and tool use. The study addresses critical limitations in LLM inputs, such as the context window and introduces Retrieval-Augmented Generation (RAG) as a solution to extend the model’s capability. Key APIs provided by OpenAI for deploying GPT models are discussed, highlighting their functionalities and applications. Finally, the practical application of LLMs in creating Robotic Process Automation (RPA) workflows is demonstrated through a divide-and-conquer methodology, showcasing the efficiency, scalability, flexibility, and accuracy of this approach. This comprehensive study underscores the transformative impact of LLMs in automating complex processes and enhancing user experiences through intelligent agent deployment.","abstract_has_math":false,"creators":["Cao, Jiannan"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"System Design and Management Program.","school":null,"contributors":[],"advisors":["Williams, John R."],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-09","date_published":"2024-09","updated_at":"2026-07-22T22:22:20Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/157177","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Williams, John R."]},{"key":"dc:contributor.department","label":"Department","values":["System Design and Management Program."]},{"key":"dc:creator","label":"Author","values":["Cao, Jiannan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-10-09T18:26:32Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-09T18:26:32Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Engineering and Management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/157177"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis investigates the deployment and utilization of Large Language Models (LLMs) as agents, exploring their potential in automating workflows and enhancing user interactions. The study begins with an in-depth analysis of language models, tracing their evolution from pure statistical models to advanced neural network architectures like Transformers and their bidirectional variants. It then delves into the operational framework of LLM agents, detailing user interactions, environmental considerations, memory management, task planning, and tool use. The study addresses critical limitations in LLM inputs, such as the context window and introduces Retrieval-Augmented Generation (RAG) as a solution to extend the model’s capability. Key APIs provided by OpenAI for deploying GPT models are discussed, highlighting their functionalities and applications. Finally, the practical application of LLMs in creating Robotic Process Automation (RPA) workflows is demonstrated through a divide-and-conquer methodology, showcasing the efficiency, scalability, flexibility, and accuracy of this approach. This comprehensive study underscores the transformative impact of LLMs in automating complex processes and enhancing user experiences through intelligent agent deployment."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["A Study on Deploying Large Language Models as Agents"]}]}],"canonical_facts":{"dc:contributor.advisor":["Williams, John R."],"dc:contributor.department":["System Design and Management Program."],"dc:creator":["Cao, Jiannan"],"dc:date.accessioned":["2024-10-09T18:26:32Z"],"dc:date.available":["2024-10-09T18:26:32Z"],"dc:date.issued":["2024-09"],"dc:description.abstract":["This thesis investigates the deployment and utilization of Large Language Models (LLMs) as agents, exploring their potential in automating workflows and enhancing user interactions. The study begins with an in-depth analysis of language models, tracing their evolution from pure statistical models to advanced neural network architectures like Transformers and their bidirectional variants. It then delves into the operational framework of LLM agents, detailing user interactions, environmental considerations, memory management, task planning, and tool use. The study addresses critical limitations in LLM inputs, such as the context window and introduces Retrieval-Augmented Generation (RAG) as a solution to extend the model’s capability. Key APIs provided by OpenAI for deploying GPT models are discussed, highlighting their functionalities and applications. Finally, the practical application of LLMs in creating Robotic Process Automation (RPA) workflows is demonstrated through a divide-and-conquer methodology, showcasing the efficiency, scalability, flexibility, and accuracy of this approach. This comprehensive study underscores the transformative impact of LLMs in automating complex processes and enhancing user experiences through intelligent agent deployment."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/157177"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["A Study on Deploying Large Language Models as Agents"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Engineering and Management"]},"updated_at":"2026-07-22T22:22:20Z"}