EMERGING TECHNOLOGY AND MODERNIZING THE ARMY
What appears to be a single AI entity is actually a team of AI entities working in tandem. These entities, known as AI agents, each specialize in one task and cooperate to provide the best response.
IMPROVING LLM RESPONSES Te AI agents in this system continue improving in accuracy over time as they interact with users. During the troubleshooting session, the conversation is logged for future analysis to determine which responses gave better results, according to user feedback. Improvements can also be made to both the existing documenta- tion and the host products based on analytics such as frequently encountered issues. Each ticket can be further processed by more AI agents to glean valuable insights on common user issues, problematic AI teams that may require additional support, or usage parameters that require adjustment. Furthermore, as systems undergo development, additional documentation can be created and ingested into the database, providing more context for answer retrieval.
However, AI tools are not without flaws. A major drawback to LLMs is their tendency to confidently provide inaccurate answers when they lack a solution. Tese inaccuracies, known as “hallu- cinations,” are especially harmful to uninformed users looking for a quick fix—users who are unlikely to immediately fact-check the response from the LLM. One method to combat hallucina- tions is to utilize RAG, grounding the context within which the LLM can scan for answers. Another method is to prompt, or instruct, the LLM to state that it cannot find an answer. Yet another method is to fine-tune the LLM, modifying and improv- ing the billions of parameters that make up the LLM to make it more accurate, though this is resource-intensive.
CONCLUSION Te integration of AI agents and LLMs into computing systems is revolutionizing the way we interact with technology and obtain support. Tese advancements are transforming customer service and technical support, making interactions faster, more efficient and surprisingly (even alarmingly) human-like. Te combination of specialized AI agents working in harmony and the sophisti- cated processing capabilities of LLMs ensures that users receive accurate, contextually rich responses almost instantaneously. As this technology continues to evolve, the lines between human
and AI interactions will blur even further, opening new possibil- ities for AI-driven assistance in a variety of fields.
Te future of AI is not just about creating smarter machines, but about enhancing our own capabilities, making expert knowledge and support more accessible to everyone. With ongoing innova- tions and increasing accessibility, AI-driven solutions are set to become an integral part of our daily lives, simplifying complex tasks and providing invaluable support wherever and whenever it is needed.
For more information, contact the Data Engineering, Architec- ture and Analysis (DEA2) team with Project Manager Mission Command (PM MC) at
peoc3t.pmmc.tmd.dea2@
army.mil.
SUNNY ZHANG is a systems engineer for the DEA2 team within PM MC, assigned to PEO C3T at Aberdeen Proving Ground. He holds a B.A. in arts, technology and emerging communications from the University of Texas at Dallas.
TURHAN KIMBROUGH is a systems engineer for the DEA2 team within PM MC, assigned to PEO C3T at Aberdeen Proving Ground. He holds an M.S. in computer science and a B.S. in computer science, both from Towson University.
ANDREW ORECHOVESKY is a senior systems engineer for the DEA2 team within PM MC, assigned to PEO C3T at Aberdeen Proving Ground. He holds a Doctor of Science from Capitol Technology University, an M.S. in cybersecurity from the University of Maryland, Baltimore County and a B.S. in information technologies from the University of Phoenix.
https://asc.ar my.mil
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