Organizations are at the heart of multi-agent systems. To deal with the ongoing dynamics and changes in the system, organizations have to adapt. Typically, agents are responsible to deal with the complexity of organization dynamics. In this paper, we present an approach for context-driven dynamic organizations in which the agent environment takes the burden of managing organization dynamics. Driven by the context, the agent environment manages the evolution of organizations and actively advertises roles to the agents, supporting the necessary collaborations between agents needed in the current context. We introduce a conceptual model for context-driven dynamic organizations and present a software architecture that supports the model in a distributed setting. The proposed approach separates the management of dynamic evolution of organizations from the actual functionality provided by the agents playing roles in the organizations. Separating these concerns makes it easier to understand, design, and manage organizations in multi-agent systems. We show how we have applied context-driven dynamic organizations in a concrete case of monitoring traffic jams. In this case, camera agents associated with traffic monitoring cameras collaborate in organizations. Depending on the context, camera agents play different roles, with responsibilities ranging from simple measurement to data aggregation. When a traffic jam covers the viewing range of multiple cameras, organizations are dynamically merged, assuring cameras detecting the same traffic jam can collaborate. Vice versa, when a traffic jam dissolves, the organization is dynamically split up. Test results indicate that contextbased dynamic organizations is a promising approach to support decentralized traffic monitoring.

Self-management is considered as one of the crucial means for software systems to deal with changing demands at runtime. Selfmanagement endows a software systems with the ability to adapt its structure or behavior without human intervention. Two different approaches are put forward for self-management: (1) the system components adapt their structure or behavior to changing requirements and cooperatively realize system adaptation-this approach can be considered as endogenous self-management; (2) the system is adapted through a control loop, i.e. the system is monitored to maintain an explicit representation of the system and based on a set of high-level objectives, the system structure or its behavior is adapted-this approach can be considered as exogenous selfmanagement.In this paper, we introduce a hybrid software architecture that combines both approaches. A multi-agent system architecture allows agents to flexibly adapt their behavior to changes in their context providing cooperative system adaptation. Then, we extend the multi-agent system architecture with a decentralized control loop adding self-healing properties to the system. We use intelligent monitoring of traffic jams as an illustrative case.