Pyth and Exa Explore New Market Data Model for AI Agents

Pyth Network and AI-native search provider Exa are collaborating to explore how AI agents can combine financial market data with web-based research, potentially opening up new approaches to data distribution, licensing and consumption as agentic AI becomes more widely adopted in financial services.

Initial applications include event-driven financial research, historical market analysis, cross-asset comparisons and portfolio monitoring. An agent investigating an earnings announcement, for example, could retrieve relevant news and company information, identify the appropriate financial instrument and analyse its price movements over subsequent trading sessions.

In conversation with Market & Alt Data Insight, Michael Cahill, CEO of Douro Labs and co-founder of Pyth Network, sees the collaboration as an opportunity to explore some of the challenges facing established market-data distribution models as financial applications become increasingly autonomous.

Rethinking Market Data Distribution

Traditional market-data infrastructure has evolved around relatively predictable consumption patterns, with financial institutions typically acquiring data through established vendor relationships, exchange agreements and application-specific licences.

While existing platforms can accommodate AI capabilities within their environments, Cahill believes companies building AI-native applications from the ground up have different requirements, particularly when it comes to sourcing and integrating information across multiple asset classes.

“The big problem with the market-data sector is that it’s a myriad of different vendors that you have to interact with to get a complete picture,” observes Cahill. “I liken it to cable television back in the 1990s. You would have a cable operator, and then you’d have channels that came as part of your bundle, and premium channels you’d have to contract for separately. That’s more or less how financial market data works today. You’ve got FactSet, Bloomberg and Refinitiv as the brokers, and then you buy data directly from exchanges. You have to go around the world to get a complete picture.”

Pyth’s approach centres on a network of financial institutions contributing pricing information, including trading firms such as Jump Trading, Jane Street, Hudson River Trading and DRW. The company The resulting Pyth-native data is made available through a common interface, reducing the need for applications to integrate multiple data feeds independently.

For an AI-native company such as Exa, this provides a way to incorporate structured financial data into its search infrastructure without having to establish separate integrations and commercial arrangements across numerous providers.

The Licensing Challenge

Existing market-data licensing arrangements generally distinguish between internal use, display, redistribution and other categories of consumption. AI introduces additional questions about how data is accessed and used, particularly when agents dynamically retrieve information, combine multiple sources and generate outputs without following predetermined application workflows.

“If you want to consume data from an internal-use perspective, that’s very non-controversial. As soon as you start getting into rights, that’s where the market ends up being the slowest and the stickiest,” notes Cahill. “If you run a large data business, you probably don’t know how to price inference, training, display and various other things that are now novel with AI consumption and usage. You’re going to be apprehensive, and that’s what we’ve seen the industry doing. This is where we can be more aggressive.”

The Exa pilot will help Pyth understand how agents consume financial data, including the frequency, volume and nature of their requests, and use those findings to develop an appropriate commercial model.

Unlike traditional data distributors that must negotiate rights with multiple underlying suppliers, Pyth argues that its contributor-based model gives it greater flexibility over how its native data can be licensed.

That flexibility could become increasingly valuable if agentic workflows generate substantially different usage patterns from conventional terminals and applications. The pilot has yet to establish which pricing structures will prove commercially viable, however.

Provenance and Trust in Agentic Workflows

Agents combining structured market observations with unstructured research need sufficient context to interpret information correctly, particularly when the underlying sources operate on different timescales or use different identifiers.

An agent analysing an earnings announcement, for example, needs to distinguish between the announcement’s publication time, the relevant trading session and the period over which a subsequent price movement should be measured. It must also identify the correct security, venue and currency where multiple possibilities exist.

The companies argue that this information needs to be explicitly machine-readable rather than relying on the interpretation that a human analyst might bring to a conventional market-data terminal.

Pyth’s infrastructure was originally developed to provide cryptographically secured pricing information for blockchain-based financial transactions, where incorrect data can have immediate and potentially irreversible consequences.

“That’s where I think our legacy of doing this for blockchain and crypto markets – dealing with machines, but with value at risk – gives us a huge advantage over something that has no comparable stakes, or is primarily designed for entertainment or display,” says Cahill.  “What we designed was a system that could withstand the fragilities of a standard publishing paradigm, while also making sure it was useful from a real-time perspective. We provide an easy-to-use source where agents don’t have to make all these inferences and decide whether the data can be trusted.”

Within the Exa collaboration, Pyth provides structured market observations and associated data context, allowing Exa’s search infrastructure to concentrate on retrieving, interpreting and synthesising information from other sources.

For institutional applications, maintaining a distinction between what an external source reports, what the market data records and what the AI model subsequently infers will be important, particularly where research processes need to be transparent and auditable.

Beyond Financial Research

Event-driven research and historical market analysis are the most immediate applications being explored, but Cahill envisages agents progressing from retrieving information and analysing market events towards constructing investment strategies and eventually initiating transactions autonomously.

He connects that development with the growth of what he calls everything exchanges – trading platforms offering access to an increasingly broad range of financial exposures, including through perpetual derivatives.

Broader cross-asset pricing coverage, increasingly continuous trading and autonomous software could eventually make it easier for agents to manage capital across different markets. Such applications would also introduce additional requirements around execution controls, governance and regulatory oversight.

For Pyth, AI-native companies that have yet to establish extensive relationships with incumbent financial-data providers represent a potential new customer base. Cahill believes these businesses may be more receptive to alternative sourcing and licensing arrangements than established institutions with substantial investments in existing infrastructure.

The Exa collaboration will provide an early indication of whether Pyth’s approach can meet those requirements. The companies are still testing consumption patterns and exploring commercial arrangements, with no production-wide deployment announced. Whether the model gains traction will depend on its ability to deliver reliable financial data at a cost and under licensing terms that make sense for AI-native applications.