Explore the Model Context Protocol for tool integration.
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Read MoreThe most valuable data in any enterprise often resides not in a modern vector store or a clean REST API, but in a battle-tested, proprietary SQL database. An AI agent that cannot access this data—be it customer history, product inventory, or financial records—is fundamentally limited in its usefulness. However, directly connecting a fleet of AI agents to a production database is an architectural anti-pattern. It would require every agent to manage database drivers, handle credentials securely, and be trusted to write safe, efficient SQL queries.
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Read MoreA purely text-based AI agent, no matter how intelligent, often leads to a frustrating user experience. An agent can identify a user's need to book a meeting, but can only respond with, "Okay, please visit our website to complete the booking." This forces the user out of the conversational flow and onto a separate webpage to perform the action. The conversation hits a dead end.
Read MoreThe Model Context Protocol (MCP) provides a powerful, standardized contract for how an AI agent invokes a tool. However, the protocol itself is, by design, transport-agnostic. The underlying transport used to deliver these standardized JSON payloads has profound implications for performance, architecture, and cost. A common architectural mistake is to force all communication over a single transport, like HTTPS.
Read MoreIn modern enterprises, the AI landscape is exploding. A company may have N distinct AI models and agents—a summarization agent, a customer support chatbot, a data analysis agent, a code generation assistant. These agents need to connect to M different tools and data sources—a Salesforce API, a production SQL database, a Stripe payment gateway, a document vector store, and a dozen proprietary internal APIs.
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