Moody’s Corp. has integrated its platform for tracking the ratings of corporations and other related research with the Amazon Quick artificial intelligence (AI) agent as part of an effort to make financial data both more accessible and simpler to navigate.

Under the terms of the alliance, 600 million public and private entities, including firmographics, ownership, financials, macroeconomic forecasts, economic data, and news sentiment used to determine credit ratings, will be made available to the Amazon Quick AI agent via a Model Context Protocol (MCP) server that Moody’s has developed and deployed.

Over time, AI agents will make it possible to embed financial data into any number of workflows, says Cristina Pieretti, head of digital content and innovation at Moody’s. The overall goal is to make financial data from a trusted source more accessible to a broader range of end users, she adds. “It changes the dynamics completely,” says Pieretti.

As data becomes more widely accessible in the age of AI, the provenance of the information being surfaced by AI models matters more than ever. There is, for example, a significant difference in the value of a report from a Moody’s analyst than an opinion that might have been expressed on a social media post that doesn’t make clear what bias helped shape it.

In the case of Moody’s, that means working with multiple providers of AI agents to ensure that financial data is being securely and safely accessed, says Pieretti. Most of the end users of Moody’s services are almost by definition risk averse so it’s crucial that whatever output generated by AI agents is validated, she adds.

Eventually, every organization that collects and analyzes data is going to want to have more control over how AI agents interact with its assets. Most of that data today sits behind a firewall that prevents it from being used to train AI models. The challenge and the opportunity is to apply AI to that data in a way that enables an organization to broaden the scope of the services they provide. Digital CxOs, naturally, will be at the forefront of many of those initiatives. The challenge and the opportunity, as always, is as much about working through the policies and guardrails that need to be put in place as it is mastering the underlying AI tools and platforms.

It’s not clear to what degree AI agents might become the primary tool used to analyze financial data but given the volume of it there’s little doubt that humans are already being overwhelmed. AI agents create an opportunity to instantly analyze datasets using a dashboard that has been specifically designed to highlight the most relevant insights. Today, far too many financial professionals are dependent on a set of general purpose dashboards that don’t always lend themselves easily to every use case.

Regardless of approach, the one thing that is certain is that as new data becomes available it’s becoming a lot easier in the age of AI to surface truly actionable intelligence versus yet another report that hardly anyone actually bothers to read simply because there is too much reliance on trailing indicators that no amount of effort is going to meaningfully change.