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The Semantic Web enables intelligent agents to ``outsource'' knowledge, extending and enhancing their limited knowledge bases. An open question is how agents can efficiently and effectively access the vast knowledge on the inherently open and dynamic Semantic Web. The problem is not that of {\em finding} a source for desired information, but deciding which among many possibly inconsistent sources is most reliable. We propose an approach to agent knowledge outsourcing inspired by the use trust in human society. Trust is a type of social knowledge and encodes evaluations about which agents can be taken as reliable sources of information or services. We focus on two important practical issues: {\it learning trust} and {\it justifying trust}. An agent can learn trust relationships by reasoning about its direct interactions with other agents and about public or private reputation information, i.e., the aggregate trust evaluations of other agents. We use the term trust justification to describe the process in which an agent integrates the beliefs of other agents, trust information, and its own beliefs to update its trust model. We describe the results of simulation experiments of the use and evolution of trust in multi-agent systems. Our experiments demonstrate that the use of explicit trust knowledge can significantly improve knowledge outsourcing performance. We also describe a collaborative trust justification technique that focuses on reducing search complexity, handling inconsistent knowledge, and avoiding error propagation.