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.

Impressive advancements in recent smart devices suggests that the direction of software engineering's future is in development of System of Systems (SoS).Among many concepts that emerged from SoS, we focus on IT Ecosystem -a type of SoS that evolves itself in response to unplanned environment changes.Maintaining autonomy of its participant systems while preserving controllability over entire ecosystem involves various challenges that we need to solve.In this paper, we propose a middleware framework for supporting global adaptation of IT Ecosystem which guides how to determine the optimal adaptation strategy for configuring available systems to satisfy local constraints while achieving global goals.To support the selection of optimal set of participant systems, we have applied genetic algorithm.The effectiveness of our approach is evaluated through analyzing the results of a simulated unmanned forest management IT Ecosystem running the proposed framework while undergoing various environmental changes.