{"id":42948,"date":"2025-07-14T18:22:32","date_gmt":"2025-07-14T22:22:32","guid":{"rendered":"https:\/\/www.bloomberg.com\/company\/stories\/closing-the-agentic-ai-productionization-gap-bloomberg-embraces-mcp\/"},"modified":"2025-07-15T14:39:13","modified_gmt":"2025-07-15T18:39:13","slug":"closing-the-agentic-ai-productionization-gap-bloomberg-embraces-mcp","status":"publish","type":"post","link":"https:\/\/www.bloomberg.com\/company\/stories\/closing-the-agentic-ai-productionization-gap-bloomberg-embraces-mcp\/","title":{"rendered":"Closing the Agentic AI productionization gap: Bloomberg embraces MCP"},"content":{"rendered":"
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After generative AI dominated headlines in 2024, 2025 is shaping up to be the Year of the AI Agent. Companies are racing to develop robust agentic AI systems that can iterate and use tools to solve complex, multi-step problems. Late last year, Anthropic released Model Context Protocol (MCP), which makes such solutions easier to build. This open standard defines how large language models (LLMs) and AI agents can interface with external tools and data sources through a shared protocol and universal interface.<\/p>\n

The cross-functional team working on AI infrastructure at Bloomberg \u2013 which includes engineers and researchers from the company\u2019s AI Engineering and Platform Services Engineering groups, technical product managers from the AI Strategy & Research team in the Office of the CTO, and members of the firm\u2019s UX Design team \u2013 has long been ahead of the curve as it relates to AI infrastructure development. They were already working on standing up an internal solution to accelerate agentic AI development before MCP was introduced.<\/p>\n

The team, which is deeply engaged in the AI and open source communities<\/a>, closely monitored developments related to MCP. Once they saw the new protocol gaining adoption among both developers and enterprises, the team quickly aligned its own efforts with MCP to boost system interoperability and developer productivity. Their ultimate goal: delivering even better AI products to clients across the finance sector.<\/p>\n\n<\/div>\n

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AI trailblazers<\/h2>\n

In its quest to deliver intelligence from the rapidly growing volume of structured data and unstructured documents in the financial markets, Bloomberg\u2019s AI journey began over 15 years ago.<\/p>\n

\u201cWe started applying machine learning to news sentiment analysis in 2009, integrated search functionality into the Bloomberg Terminal by 2012, and built and deployed neural networks to discover news themes in 2020,\u201d says Engineering Manager Sambhav Kothari, Head of AI Productivity in Bloomberg\u2019s AI Engineering group.<\/p>\n\n<\/div>\n

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Engineering Manager Sambhav Kothari, Head of AI Productivity in Bloomberg\u2019s AI Engineering group<\/figcaption>\n<\/figure>\n
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Kothari leads a group of researchers and engineers whose goal is to help ensure that Bloomberg\u2019s AI application teams can productively use our AI infrastructure to deliver innovative applications. In turn, these AI solutions enable Bloomberg\u2019s clients to make smarter, more informed business and investment decisions.<\/p>\n

\u201cWhen generative AI went mainstream in 2022, we already had an infrastructure mindset and were comfortable tackling the challenges to scale this technology,\u201d says Kothari.<\/p>\n

In early 2024, the team was deeply engaged in researching the opportunity to scale generative AI to support enterprise applications. In particular, they focused on the specific challenges of productionizing GenAI in finance responsibly. Some of the things they needed to address included how to incorporate continuous evaluation, guardrails, and traceability into their systems.<\/p>\n

\u201cThis \u2018productionization gap\u2019 was killing our velocity,\u201d notes Ania Musial, Head of AI Platforms in Bloomberg\u2019s Office of the CTO. \u201cTeams could build impressive demos in days, but it took much longer to get them production-ready for clients to use.\u201d<\/p>\n\n<\/div>\n

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Ania Musial, Head of AI Platforms, CTO<\/figcaption>\n<\/figure>\n
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Parallel-pathing an emerging protocol<\/h2>\n

The dynamic nature of agentic AI systems represents a scalability challenge. \u201cIn order for us to create maintainable systems, we need to regularly evaluate, improve, and add new capabilities to the agents. These systems are anything but static,\u201d says Musial.<\/p>\n

The team realized that the ability to make AI tools \u2013 the modular capabilities used by agents to gather information or take actions \u2013 \u201cplug-and-play\u201d was the missing piece for enterprise-scale agent development. \u201cAgents need to interact with everything else in your system: tools, other agents, applications, and LLMs,\u201d explains Kothari. \u201cWhen you have hundreds of AI researchers and engineers building multiple applications across numerous business areas, you can’t just hard-code dependencies. You need configurable, swappable, loosely coupled components.\u201d<\/p>\n

The team hypothesized that protocol-based support for tools \u2013 along with improved discoverability and well-defined integration points \u2013 would help speed up the development of production-ready AI applications. Standard protocols enable dependency inversion, which can spur rapid innovation. For example, HTTP standardized communication between browsers and web sites, which led to the emergence of a global web development ecosystem. Similarly, in the GenAI era, the convergence across the industry towards the OpenAI API standard made swapping between LLMs more seamless.<\/p>\n

By Q3 of 2024, the Bloomberg team had built a prototype of its GenAI tools protocol with a server SDK, middleware, and infrastructure components. They were proving out their hypothesis with real-world implementations.<\/p>\n\n<\/div>\n

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“Building better agents depends on swapping out clouds, hardware, models, providers, and application interfaces.”<\/strong><\/em><\/p>\n

\u2013 Ania Musial<\/strong><\/p>\n<\/blockquote>\n\n<\/div>\n

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Further corroboration of the team\u2019s hypothesis took place when Anthropic introduced MCP.<\/p>\n

\u201cFrom day one, we closely followed MCP’s progress because we realized this protocol had the same semantic mapping as our internal approach, but it was being built in the open,\u201d says Kothari. \u201cWe had seen this play out before \u2014 when open source standards emerge and get adopted widely, they create network effects that benefit everyone. We quickly recognized that MCP had that same potential.\u201d<\/p>\n

The second iteration of the MCP specification in March 2025 added support for Server-Sent Events (SSE) and streamable HTTP, making it scalable for the web. \u201cWe predicted MCP would create a wave of remote servers, which was validating for our approach,\u201d says Kothari.<\/p>\n

When OpenAI, Google, and Microsoft all announced plans to adopt MCP, it enabled the Bloomberg team to use its internally built tools with a variety of third-party client applications without needing to build any additional custom integration points.<\/p>\n

After seeing the direction in which MCP\u2019s potential was unfolding, the Bloomberg team converged its internal approach with the open standard. \u201cBuilding better agents depends on swapping out clouds, hardware, models, providers, and application interfaces,\u201d said Musial. \u201cThe more interoperable and composable these elements become, the more nimble we can be as platform engineers, application developers, and as a product team. We were thrilled to see this was going to be embraced as the industry standard.\u201d<\/p>\n\n<\/div>\n