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Last year, we spoke about our adoption of Model Context Protocol (MCP) as a foundation for interoperable AI agents and tools at Bloomberg. What we’ve learned since then is that interoperability is the easy part; building trustworthy and interoperable AI infrastructure is the hard part.
Today, MCP powers Bloomberg’s flagship agentic AI solution (ASKB), underpins the experience the firm’s engineers have with our AI development platform, and drives production workflows across Bloomberg. Scaling all of this required more than standardizing tool calls.
In this update, we’ll share how our approach to building trustworthy agentic AI infrastructure down to its core: interceptors to constrain & guide agentic behavior, tool variants to support different models and surfaces, and governance strong enough to let teams innovate quickly without compromising control. Interoperability may get your AI agents connected, but trustworthiness is what enables your agentic AI systems to scale.
