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Multi-agent systems (AMS) are developed using a variety of architectures. These range from peer-to-peer to blackboard-like communication, from insecure distributed systems to complex security schemes, from those MAS using databases to those using knowledge based information systems. Although a system's architecture often has to be designed according to its specific application domain, we present a reliable MAS architecture suitable for most purposes in the e-business domain. This framework combines the distributed nature of a multi-agent system with the security and encryption facilities provided by Web servers, separating data from knowledge and encouraging the use of distributed data structures in a concurrent, scalable, transaction-safe and remote-event generator. Notes on agent reputation assessment are also included. We also endow our agents with learning capabilities for two different aspects: reinforcement learning for bidding and a concept learning technique for building user profiles. Reinforcement learning allows for agent adaptive bidding whereas concept learning is of key importance to agent service bundling. The main features of the proposed architecture are agent communication through a common area, remote user access through servlets with authentication and encryption, data/knowledge separation and agent reputation assessment using customer satisfaction values. The functionality of all the proposed multi-agent system features is illustrated through an agent-based travel services application (MASTAS).