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- Design e implementação de um conector seguro e escalável compatível com MCP para acesso de agentes de IA a dados de Business IntelligencePublication . GRAÇA, DANIEL ALEXANDRE RIBEIRO; Duarte, Fernando Jorge FerreiraThe increasing adoption of Large Language Models (LLMs) and agent-based systems in enterprise environments has intensified the need for secure, scalable, and standardized mechanisms that allow Artificial Intelligence (AI) to interact with structured business data. In the retail domain, Business Intelligence (BI) systems play a central role in supporting operational and strategic decision-making, yet their integration with autonomous AI agents remains fragmented and largely ad hoc. This dissertation addresses this gap by designing, implementing, and evaluating a secure and scalable integration layer that enables AI agents within Watson (the AI decision-support platform developed by Omnium:retail) to access enterprise data through declarative tools and governed enterprise services. The solution is MCP-compatible in contract and architecture: enterprise capabilities are exposed with JSON Schema contracts and validated before execution, following the declarative tool-integration model promoted by the Model Context Protocol (MCP). Watson does not implement an MCP server or client; interoperability is achieved through an internal registry-based connector and OpenAI function calling rather than MCP transport. The work combines a systematic literature review (PICOCS and PRISMA) with architectural modeling and an empirical consistency study. Across 390 independent executions covering menu navigation, stock lookup, and product-information scenarios, 98.2% of responses were semantically correct and grounded in tool outputs. Mean end-to-end latency (∼9 s), however, remained well above the two-to-three-second interactive target envisaged at project ideation, reflecting the cost of multi-stage LLM orchestration in a planner–executor pipeline. The state-of-the-art analysis and evaluation demonstrate that an MCP-compatible integration layer can support reliable agent access to enterprise data in a multi-tenant setting when complemented with appropriate architectural patterns, access control, and action validation. This work contributes to the practical understanding of MCP-aligned integration in enterprise contexts and provides an empirically grounded foundation for future optimization within production retail environments.