Service-Oriented Architecture (SOA) structures applications into collections of modular, independent, and reusable services. We propose an SOA-based intelligent service agent framework for building AI applications that decomposes complex tasks into independent functional units. In the framework, the agent operates as an intelligent executor that dynamically orchestrates and invokes diverse services and tools to achieve its goals. The agent is exposed as a self-contained service with a well-defined API, allowing external applications to invoke it directly. By instrumenting requests and responses at both the service and agent layers, the framework enables tracing of the agent’s capabilities, performance, and decision-making. We present the design of an operational scheme for the agent with DID handling, verifiable credentials (VC), and verifiable presentations (VP). Each of the agents collaborates on a shared workspace based on blackboard to handle tasks to reach a goal. Finally, we demonstrate its feasibility through a proof-of-concept (PoC) for Agentic AI service architecture. This proof-of-concept, structured across Phase 1 (discovery, verification, and scoped authorization) and Phase 2 (problem posting and blackboard-mediated collaboration), demonstrates that DID-backed credentialing can securely support multi-agent execution under a least-privilege operational model.

It is challenging to understand Literary Sinitic text from the Joseon dynasty, since there is a lack of explicit word separators, which creates significant semantic ambiguity. To address this, both sentence segmentation and named entity recognition (NER) are essential. We propose a Transformer-based analyzer that performs these two tasks simultaneously. Trained on a labeled corpus from the Seungjeongwon Ilgi, our model effectively segments sentences and identifies named entities, thereby significantly improving the understanding of sentence structure and overall context.

Emu lab is an emulation-based network test-bed constructed for research and education. It is used for building and testing applications in fields of information security and computer network. The application of Emu lab is being extended to parallel processing of scientific data. The DNA sequence search is one of major research areas in the bio-informatics. With the high-performance computing, the biologists can get their results easily and faster. mpiBLAST and mr-mpi-blast can process DNA sequence alignment on parallel computer. In this research, the research environments for mpiBLAST and mr-mpi-blast are built on Emu lab. Also, the DNA sequence alignment is performed with NCBI database. This research shows that the Emulab is an effective environment for the research of the bio-informatics.