How an AI agent trades prediction markets for the first time

An AI agent trading prediction markets acts as a software delegate that reads event probabilities, places orders sized in plain dollars, and manages positions through a single API, while you retain full control of the funds and set hard limits it cannot exceed. It does not predict the future. It automates the mechanical work of monitoring prices, calculating desired exposure, and sending orders to a venue, all within constraints you define before it starts. For someone who has never automated a trade, the shift is from clicking buttons to writing rules that the agent enforces without hesitation, so precision cuts both ways.

What is a prediction market agent?

A prediction market is a venue where participants trade contracts that pay out based on the outcome of real-world events. Prices in these markets are often interpreted as the crowd's estimate of probability. The contracts typically resolve to a value between zero and one, or they pay a fixed amount if a specific outcome occurs. An AI agent that trades these markets is simply a program connected to an API that can read those prices and submit buy or sell orders on your behalf. The agent does not need to understand politics, sports, or economics in a human sense. It needs a strategy, which can be as simple as a threshold rule, and a connection to the market.

Felix provides one API and one key that connects an agent to multiple market types, including prediction markets. The agent can be a local script, a cloud function, or an AI assistant inside an editor like Claude or Cursor that interacts through MCP tools. You do not need to build a full application. You need a decision rule, a budget, and a way to tell the agent when to act. The agent then handles the repetitive work of checking prices, formatting orders, and tracking fills. You can think of it as hiring a very fast, very literal clerk who follows your written instructions exactly.

Because the agent acts on your behalf, it is important to separate what it controls from what you control. The agent can spend money within a scope you define, but it cannot withdraw funds to an external address you have not approved. This is the core of a non-custodial setup. The wallet remains yours. The agent is a spender with a strict allowance, not a custodian of your capital. If the agent is compromised, the damage is bounded by the scope you set in advance.

How does the agent place its first trade?

Before the agent trades, you define its scope. You set a total budget cap, a maximum position size per market, and which markets it is allowed to access. You also define the strategy. For example, you might instruct the agent to buy shares if the implied probability drops below a certain level, or to sell if it rises above another. These are your rules. The agent enforces them literally. It does not add nuance. If you say buy when the price is below fifty cents, it will buy at every tick downward until your limit is reached.

When the agent runs, it polls for market data or receives updates. It reads the current prices, which the API normalizes into plain dollar values so you do not need to think about contract ticks or decimal places. If the price meets your rule, the agent constructs an order. The order size is expressed in dollars, not in native contract units. The API converts that dollar amount into the correct number of shares or contracts for the specific venue. This abstraction removes a common source of manual error.

The agent then submits the order. If the order fills, the agent records the fill price and size, updates its internal model of your current position, and waits for the next signal. If the order fails, perhaps due to insufficient liquidity, the agent logs the failure and retries according to your parameters. You can observe this behavior through audit logs, which create a permanent record of every quote read and every order sent. How AI agents execute orders through MCP covers the mechanics of this connection in more detail.

The first trade is usually the hardest to watch because you are not pressing the button. You are watching a program spend real money based on rules you wrote earlier. That is why paper trading exists. You can run the agent in a simulation environment where orders are tracked but no real money moves. This lets you observe how strictly the agent follows your rules, how quickly it reacts, and whether your logic contains edge cases you did not anticipate. Many beginners discover that their agent trades far more often than expected, or that it fails to account for fees, only during paper trading.

What keeps your money safe if the agent makes a mistake?

The most common fear for new automation users is that the agent will run away and lose everything before they can react. A properly configured agent uses several overlapping controls:

• ·Non-custodial funds. The wallet remains yours. The agent receives a scoped key that can place orders, but it cannot withdraw funds to an arbitrary address. Withdrawal addresses are owner-approved only. Even if the agent is compromised, the attacker cannot steal your capital. They can only trade within the limits you set. How non-custodial trading keeps your money safe explains this architecture in depth.
• ·Infrastructure-level budget caps. You set budget caps and position limits before the agent starts. The API enforces these, not the agent. A buggy or hallucinating agent cannot talk its way around the limit. If the cap is ten thousand dollars, the API rejects the eleventh order.
• ·Exit plans and a kill switch. You define conditions that automatically flatten positions and pause trading. You also have a panic switch that revokes the agent's key instantly and cancels open orders. You set these before the first trade. The kill switch does not ask for confirmation. It acts immediately.
• ·Audit logs and observability. You can see the agent's reasoning, order history, and current exposure in real time. If behavior drifts, you notice it quickly. You can also reconstruct exactly what happened after a problem.

Safety is not a single feature. It is a stack of overlapping controls that limit the damage any single failure can cause.

How should you size positions when the agent decides for you?

Position sizing is the process of deciding how much money to allocate to a single trade. In manual trading, you might look at your account balance, think about the risk, and type in a number. An agent does not think. It needs a rule. If you do not give it one, it may size too large during a streak of confidence or too small during quiet periods. The result is often an erratic exposure profile that looks nothing like what you intended.

The simplest approach is to set a fixed dollar amount per trade. You might tell the agent to risk no more than one hundred dollars on any single prediction market. This is easy to understand and easy to audit. A more dynamic approach is to size as a percentage of the remaining budget. If the budget is ten thousand dollars and you set a two percent per-trade limit, the agent sizes the first trade at two hundred dollars. If the budget drops, the size drops automatically. This creates a natural de-risking mechanism as losses occur.

You should also set a maximum open position limit per market and in total. The agent might want to buy more as the price moves, but if you cap it at five hundred dollars per event, it stops adding exposure once that limit is reached. This prevents a single market from dominating your risk. It also prevents the agent from averaging down repeatedly into a losing position that eventually consumes your entire budget.

It is important to remember that prediction markets can resolve to zero or one. A position can become worthless if the outcome you bet against occurs. You should size every trade as if the entire position could be lost. The dollar sizing abstraction helps with this, because you can look at the order and ask whether you are comfortable losing that exact amount. Position sizing for an AI trading agent from first principles offers a framework for turning those constraints into durable rules.

What do most beginners get wrong about LLM trading?

Many beginners assume that an AI agent trading with real money is primarily an intelligence problem. They believe the hard part is building a model that predicts events better than the market. In practice, the hard part is usually execution safety and risk management. An agent with a mediocre strategy and excellent safety controls will survive longer than an agent with a brilliant strategy and no guardrails. The market can stay irrational longer than an agent can stay solvent if that agent is over-leveraged.

Another common mistake is conflating the language model's reasoning with trading authority. A large language model can explain why a market might move, but that explanation is not a guarantee. The model does not have insider information. It is processing patterns in text. If you let the LLM both decide and execute without hard limits, you are trusting a text generator with your bank account. The correct division of labor is that the LLM proposes or explains, and the hard-coded limits enforce. The agent executes within a cage you built.

Beginners also underestimate the emotional adjustment of watching an agent trade. When you click manually, you feel in control. When an agent trades, you feel passive. This passivity can lead to two errors: either you panic and shut down a valid strategy after the first loss, or you ignore the agent and fail to notice when market conditions have changed. You still need to monitor, maintain, and occasionally intervene. Automation does not mean absence. It means the routine work is delegated, but the responsibility remains yours.

Finally, some people think they need a large budget to make agentic trading worthwhile. In reality, a small budget is often better for learning. The agent behaves differently with limited capital, and the lessons you learn about sizing and speed are cheaper at small scale. How an agent trades a small budget differently than manual trading describes those dynamics. Starting small is a feature, not a limitation.

How do you move from paper trading to real money?

Paper trading is a simulation mode where the agent runs through the full logic of reading markets, placing orders, and tracking positions, but no real money is transferred. This is the correct place to start. You should run the agent long enough to see how it handles price gaps, partial fills, and conflicting signals. Look for bugs in your logic, not just profits. A strategy that looks perfect in a spreadsheet often reveals hidden flaws when it meets real market data and timing constraints.

When you are ready to trade with real money, you create a live key. The system requires explicit owner authorization before this key is activated. There is no accidental graduation from paper to live. You must consciously approve the key, set its budget, and define its scopes. Once live, the agent uses the same API and the same order logic, but the fills now move real dollars. The transition is intentional.

It is wise to start with a live budget smaller than your paper budget. The psychological difference of real money often changes how you perceive the agent's behavior. A drawdown that felt trivial in simulation may feel significant when it is real. A smaller live budget lets you adapt to that feeling without meaningful financial stress. You can increase the budget later as your confidence in the agent grows. The goal of the first live phase is not to make a fortune. It is to prove that your safety stack works under real conditions.

You should also establish a review schedule before going live. Decide in advance when you will check the logs, what metrics you will track, and under what conditions you will pause the agent. Writing these rules down while you are still in paper mode reduces the chance of an emotional decision after a losing day. The agents that survive are the ones whose owners planned for adversity before it arrived.