Building Reasoning AI Agents with Live Data: A Trading Advisor Case Study

Most systems that work with real-time data don’t really help you think. They throw numbers at you, charts, metrics, indicators, percentages. Whether it’s RSI and SMA in trading, dashboards full of KPIs, or operational metrics in a production system, the experience is often the same. You’re left staring at the data and still asking:

“So… what does this actually mean, and what should I do next?”

That’s the real problem. It’s not a lack of data, it’s a lack of clear, explainable reasoning on top of that data.

People don’t want raw statistics. They want systems that can look at live signals, connect the dots, and explain decisions in plain language. That’s where AI agents become useful. Instead of simply displaying numbers, an AI agent can:

In this article, we’ll focus on the fundamentals of building AI agents that reason over real-time statistics. To make the idea concrete, we’ll walk through a practical example: a trading advisor agent that analyses traditional market indicators and explains whether a stock looks like a BUY, HOLD, or SELL.

This agent doesn’t replace experts or make promises about outcomes. It supports decision-making by making reasoning visible. By the end of this article, you’ll understand how to design this kind of agent using Lamatic.ai and expose it as a reusable API.

Real-time statistics are everywhere. Businesses track performance metrics, engineers monitor system health, analysts watch trends, and traders follow market indicators. But numbers alone don’t answer questions, interpretation does.

A dashboard can tell you what is happening, but it rarely explains:

This is where AI agents add value. An agent doesn’t just retrieve live data. It reasons over that data using rules, patterns, and contextual logic, then turns signals into explanations.

At a high level, this pattern looks like:

This pattern applies across domains, analytics, operations, research, customer support, and anywhere decisions depend on live data. In the rest of this article, we’ll apply this pattern to trading as a concrete example.

In trading, real-time statistics often come in the form of technical indicators. These indicators don’t predict the future, they describe recent behavior and provide signals that traders interpret to guide decisions.

From an AI agent's perspective, indicators are simply structured signals. In other domains, these might be KPIs, usage metrics, error rates, or behavioral data. The mechanics are the same, raw signals that need interpretation.

For this example, we’ll work with three commonly used indicators:

SMA (Simple Moving Average): SMA shows the general trend, whether a stock is moving upward or downward over time.

RSI (Relative Strength Index): RSI measures whether a stock may be overbought or oversold. A high RSI can indicate overheating, while a low RSI may suggest a potential rebound.

OBV (On-Balance Volume): OBV tracks volume flow to determine whether money is moving into or out of a stock. It helps answer a key question:“Are traders accumulating, or quietly exiting?”

Disclaimer: This content is for educational and informational purposes only. It does not constitute financial, investment, or trading advice. The AI agent demonstrated in this article is a technical example built using Lamatic.ai and should not be used to make real trading or investment decisions.

Before diving into tools or configuration, let’s step back and look at the core idea.

We are building an AI agent that:

To make this concrete, we’ll implement this pattern as a trading advisor. The agent:

Here’s how the system works end to end:

User → Lamatic AgentKit → Alpha Vantage APIs → LLM

The journey starts with the user, you type something like:

“AAPL”: AAPL is the symbol for Apple stock.

That input is sent to a Lamatic AgentKit. Lamatic acts like the conductor of an orchestra:

For this article, we will be using the Alpha Vantage API. Once the agent receives the request, it fetches real trading data using multiple APIs:

After all the data is collected, it’s passed to a large language model (LLM). This is where the “thinking” happens. The LLM:

Before moving into the setup details, it’s worth noting that the following steps describe one concrete implementation of this agent pattern using Lamatic.ai. The same reasoning flow can be adapted to other domains by swapping the data sources and domain logic.

Lamatic.ai is a managed AI middleware platform (PaaS) designed to help engineering teams build, deploy, and manage agentic AI applications. You can sign up here.

Every agent needs a starting point. In Lamatic, this starting point is called a trigger.

For this trading advisor, the flow is triggered using an API Request node.

When creating a new flow in Lamatic Studio, the first step is to click “Choose a Trigger” and select API Request. This tells Lamatic that the agent should start running whenever an external system makes an HTTP request to it.

Below is the exact flow used to build the trading advisor. To implement this project, we used 7 nodes.

This is where everything begins.

The input schema defines what your API needs from the user.

In this project, the input is simple

Confirm Response Type is set to realtime.

Next, the agent fetches Simple Moving Average (SMA) data.

This step:

After SMA, the agent fetches Relative Strength Index (RSI) data.

This tells the agent:

Next comes On-Balance Volume (OBV).

This flow:

Here, the agent fetches recent trading data.

This adds context:

This is where everything comes together. All the data collected so far is passed into an LLM node with a clear instruction:

Click on the MODEL dropdown, and select + Add Provider

Update the Credential Name and select your desired model Provider

The LLM doesn’t just receive one big prompt. It works with two different prompts, each with a clear role:

Finally, the agent prepares the output.

The response flow:

The output schema defines what your app receives back.

Instead of raw data, the response is structured and readable.

Select the arrow next to the Test button (located at the lower right of the display).

Enter a test symbol, for example:

Click the Test button to run a test of the flow.

Under Execution, you can see the flow debugger display the Input, Output, and Logs for each node. Green indicates that your flow is working without errors.

Click the Deploy button in the upper right to deploy your Flow.

Finally, your project is deployed.

Once the flow is deployed in Lamatic, it becomes a callable service with a stable API endpoint. At this point, you can integrate it into your application using the Lamatic SDK, rather than making raw HTTP calls.

The SDK handles:

In this project, the integration layer is intentionally simple. A frontend (for example, a Streamlit app) collects user input like a stock symbol, then uses the Lamatic SDK to invoke the agent. The SDK sends the request to the deployed flow, waits for the agent to complete its reasoning, and returns the final response in a format the app can immediately display.

This approach has a few advantages:

Because the agent is exposed through a standard interface, the same SDK-based integration can be reused across:

By combining real-time statistics with an AI agent, we move from “here are the numbers” to “here’s what the numbers mean, and why.” The agent doesn’t replace experts or make promises about outcomes. Instead, it structures reasoning, connects signals, and explains decisions in plain language.

Lamatic.ai makes this approach practical. Rather than stitching together scripts and services manually, you can design agents as flows that:

Once deployed, an agent becomes a reusable building block. You can plug it into a Streamlit app, a dashboard, a chatbot, or another system entirely.

While we used a trading advisor as a concrete example, the pattern extends far beyond finance. The same approach applies to analytics, operations, customer support, research, and decision-making workflows. AI agents aren’t about replacing expertise, they’re about making reasoning visible, explainable, and accessible.

If you would like to explore further or build on what we covered in this article, the resources below will help you go deeper and move faster:

Together, these resources give you everything you need to go from idea to deployed system. Whether you’re extending this trading advisor or applying the same pattern to a different domain, the tooling stays the same, only the logic changes.