Polymarket never stops.

Markets heat up. Activity suddenly accelerates. Buying pressure turns into selling pressure. A market that looked quiet an hour ago can suddenly attract a wave of participants. A dramatic move can be supported by broad activity, or driven by only a handful of large participants.

All of that information is there.

The problem is figuring out what actually matters.

Today, we're making the Polymarket Intelligence Agent publicly available for anyone to try, for free.

Try the agent → https://pm.deltastream.io/

If you use Polymarket regularly, the idea is simple: instead of trying to watch everything yourself, ask the agent what you need to know.

What if you could just ask Polymarket what changed?

Imagine opening Polymarket in the morning after being away for eight hours. There are markets everywhere. Prices have moved. Volume has changed. Thousands of transactions have happened.

Where do you start?

With the Polymarket Intelligence Agent, you can simply ask:

“What changed while I was away?”

Instead of giving you another list of popular markets, the agent can tell you that one market went from emerging buying pressure to sustained activity, another experienced a flow reversal, participation in a third suddenly broadened, and a fourth began showing unusual activity from wallets newly observed in the available history.

Or ask:

“What is waking up right now?”

The agent can find markets where activity is suddenly accelerating relative to their recent pace, then explain whether the activity is supported by increasing participation and persistent directional pressure.

Or perhaps you see a market exploding and ask:

“Is this actually a strong signal?”

The interesting answer may be: the activity is extremely strong, but the quality of the signal is low because most of it is concentrated among a small number of participants.

That distinction matters.

A traditional leaderboard can tell you what is big. This agent is designed to help explain what is changing, how it is changing, and how much confidence you should place in the structure of the observed activity.

Watch the Polymarket Intelligence Agent in action

https://youtu.be/FHtLeMErBXs

Polymarket doesn't have a data problem

There is already an enormous amount of data available around Polymarket.

You can see markets, prices, volume, transactions, wallets and market metadata. There are dashboards, APIs, block explorers and analytics tools that expose more information than any individual could reasonably follow.

And that is exactly the problem.

Raw data is not context.

Suppose a trade arrives for a market. By itself, that trade doesn't tell you very much.

To understand whether something meaningful is happening, you may need to know how much activity occurred over the last five minutes, how that compares with the last hour, whether activity is accelerating, whether buying or selling pressure has persisted across several time windows, how many wallets are participating, whether participation is expanding, whether a few large fills dominate the activity, and how the current behavior differs from what was happening before.

Now repeat that continuously across thousands of assets and participants.

The useful information isn't contained in any single event.

It has to be computed from events over time.

And that is where this agent becomes fundamentally different from simply connecting an LLM to an API.

An AI agent shouldn't reconstruct the world every time you ask a question

A common approach to building data-driven AI agents is to give the model tools for accessing raw data.

The user asks a question. The agent makes API or database calls. It retrieves records. Then it tries to figure out what those records mean.

That works surprisingly well for simple questions such as, “What is the current state of this market?”

It becomes much harder when the question is:

“What changed?”

Now the agent needs history.

Or:

“What is accelerating?”

Now it needs multiple time windows and comparisons.

Or:

“Is this move broad or concentrated?”

Now it needs to understand activity across participants.

Or:

“Which signals became stronger while I was away?”

Now it needs to reconstruct previous states, compare them with the current state, and determine which changes are meaningful.

You could ask an LLM to repeatedly assemble all of this from raw data at runtime. But now the model is spending its time and tokens trying to do the work of a streaming data system.

We think there is a better approach.

Compute the context continuously, then let the agent reason over it.

That is what DeltaStream does for the Polymarket Intelligence Agent.

The agent doesn't start with raw events. It starts with understanding.

Behind the agent, DeltaStream continuously maintains fresh, time-aware context from Polymarket activity.

It isn't waiting for someone to ask a question before figuring out what happened.

The context is already being maintained.

Activity across different time horizons is continuously computed. Acceleration is continuously updated. Directional flow is tracked. Participation breadth and concentration are maintained. Signal strength and signal quality are evaluated. Changes between previous and current states are preserved so that transitions such as emerging pressure, sustained activity and reversals can be identified.

So when you ask:

“What matters right now?”

the agent doesn't need to start from a giant collection of transactions and reconstruct the answer.

The important context already exists.

The AI can do what AI is actually good at: understand your question, decide which context matters, reason over it and explain the result in language anyone can understand.

That difference, between retrieving raw data and providing fresh context, becomes increasingly important as agents take on more useful jobs.

Polymarket is just one example of a much larger category of AI agents


The Polymarket Intelligence Agent makes this problem especially easy to see because the world it observes changes constantly.

But Polymarket isn't unique.

Consider a cybersecurity agent trying to answer, “What changed in our environment and what should I investigate?”

A payment operations agent trying to determine, “Which transactions are becoming risky or are likely to fail?”

A travel operations agent answering, “Which disruptions are getting worse and which customers are affected?”

A commerce agent deciding, “Which checkouts need intervention right now?”

In every case, giving the agent access to databases and APIs is only the beginning.

The agent needs current state, recent history and time-aware signals derived from constantly changing raw data.

We call this fresh data context.

For an important class of AI agents, fresh context isn't an enhancement to the agent.

It's what makes the agent useful.

Building that context shouldn't require building an entire data platform

There is another side to this problem.

Building the Polymarket Intelligence Agent required much more than defining an AI prompt.

Someone has to continuously process incoming events, maintain state, calculate rolling windows, join changing datasets, preserve history, detect transitions, materialize the resulting context and make it available to the agent with the right access controls.

Traditionally, building that layer means assembling and operating multiple pieces of streaming and data infrastructure.

DeltaStream is designed to make that unnecessary.

With DeltaStream, teams can build the continuously updated context their agents need and expose that context directly to agents through DeltaStream's built-in MCP server.

And we're making the process even simpler with DeltaStream Agent.

Instead of requiring someone to become a streaming expert and manually write every data pipeline, you can describe the context you want in natural language. DeltaStream Agent can help generate the real-time pipelines needed to create and continuously maintain it.

You describe what the AI agent needs to know.

DeltaStream helps build the machinery that keeps that knowledge fresh.

That changes who can build these kinds of applications. A developer can move dramatically faster, and users who aren't experts in streaming systems can begin creating sophisticated real-time context without first learning how to design and operate the underlying infrastructure.

The goal isn't to make everyone a streaming engineer.

The goal is to make fresh context easy enough that every useful AI agent can have it.

Try the Polymarket Intelligence Agent

We built this agent because we wanted to demonstrate what an AI experience feels like when the model doesn't have to discover the state of the world from scratch every time you ask a question.

Try asking it:

What should I pay attention to right now?

Then:

What is waking up?

Which signals are strong but fragile?

Where is participation broadening?

Are there any flow reversals?

Is unusual participant activity appearing anywhere?

And then come back later and ask:

“What changed while I was away?”

That last question captures what we believe is one of the biggest opportunities for AI agents.

The future isn't just agents that can search data.

It's agents that understand what is happening now, what happened before, what changed, and why that change matters.

That's the experience fresh context makes possible.

And now you can try it yourself.

Try the Polymarket Intelligence Agent for free → https://pm.deltastream.io/

Want to build an agent like this for your own real-time data and use case?

DeltaStream can build the complete solution, from continuously maintained fresh context to the AI agent that uses it.

Build with DeltaStream → https://www.deltastream.io/contact-us/