How to evaluate prediction market trading with an AI agent when you have never automated a trade

Prediction market trading with an AI agent does not require prior automation experience, but it does require understanding how event contracts work and what non-custodial infrastructure actually controls. An agent can buy and sell outcome shares on your behalf within strict spending limits you set, yet it cannot withdraw funds or move capital to addresses you have not approved. Before you begin, you should recognize that prediction markets resolve to binary or discrete outcomes, which means traditional technical analysis and continuous price charts often matter less than timing, liquidity, and event resolution logic. If you have never automated a trade, your first step is not to find a strategy, but to understand how the agent interacts with the venue and how you retain control of the money. You should also accept that trading can lose money, including your entire allocated budget, and that no automation framework can guarantee a profit.

What makes prediction markets different from stocks or crypto?

A stock represents a claim on future earnings and can be held indefinitely. A crypto token may trade on network usage, monetary policy, or speculative demand. A prediction market contract, by contrast, is a bet on a specific event with a defined expiration and a fixed payout structure. Prices are bounded, often between zero and one dollar, and they reflect the market's consensus probability rather than a discounted cash flow or network value. This changes how an agent should think about value, and it changes how you should evaluate the agent's performance.

Liquidity in a prediction market is typically event specific and time varying. A popular political or sporting event may attract volume early, then see spreads widen as informed traders exit and casual participants remain. An agent that places market orders without checking depth may receive fills at prices far from the last trade. In thin markets, a single large order can move the implied probability dramatically, and the agent may inadvertently pay a premium that eliminates any edge. You should consider whether the agent is aware of order book depth, or whether it is simply issuing buy and sell instructions blindly.

Resolution risk is another factor that does not exist in the same form for stocks or perpetual futures. The venue determines the final outcome, and that determination can be delayed, disputed, or based on data sources that change. If your agent holds shares through resolution, the position converts to a binary payoff. There is no averaging down or waiting for a recovery. The shares become worth one dollar or zero. Because of this, the concept of a stop loss operates differently. You cannot simply exit at a technical level if the event resolves against your position while you sleep. The market may close before you have a chance to react.

The agent's logic therefore needs to account for time decay, binary payoffs, and event driven volatility. A model that works for trend following in equities may misinterpret a probability spike as a breakout and buy into a temporary liquidity gap. You should evaluate whether the agent's reasoning process, whether prompted in natural language or coded as rules, explicitly models the event timeline and the possibility of sudden resolution. If the agent does not understand that the contract expires, it may treat the position like a perpetual asset and fail to exit before the window closes.

How does an AI agent place a trade without taking custody?

When you automate through Felix, your funds remain in a wallet that only you control. The agent receives a scoped API key that authorizes it to send orders, read balances, and check positions, but it cannot add withdrawal addresses, change ownership, or move funds to itself. Withdrawal addresses are owner-approved only, and this non-custodial design is enforced by the infrastructure rather than by policy. The agent can spend within the limits you set, but it can never steal the capital. This is a structural guarantee, not a contractual promise.

Agents connect through MCP tools or the REST API. If you use Claude, Cursor, or another MCP client, you can describe what you want in plain language and the agent translates that into structured API calls. If you prefer, you can write direct REST requests. Either way, orders are sized in plain US dollars, and the API normalizes venue-specific contract math. You do not need to calculate how many shares equal fifty dollars, or what the tick size is, or how collateral is posted. The system abstracts that away and reports fills back to you in dollars. This removes a common barrier for beginners, who often struggle with the idiosyncratic contract specifications of each venue.

This unified approach applies across all five market types. One key and one API let you trade stocks, crypto, perps, options, and prediction markets. For a beginner, this means you do not need to open separate accounts, learn different margin systems, or manage distinct custody models for each asset class. The same scoped key, the same budget cap, and the same kill switch govern every market. You can learn one interface and apply it to prediction markets without first becoming an expert in how each venue handles share registration or settlement. The trading API for AI agents explains how this abstraction works across market types.

What safety controls should a beginner configure first?

Before the agent sends its first order, you should configure safety controls that act as hard boundaries rather than gentle suggestions. These layers work together to limit what the agent can do, how much it can spend, and how it can exit.

• ·Scoped API keys restrict which markets the agent can access, which order types it can use, and the maximum dollar value per trade. This prevents the agent from accidentally submitting a large market order because it misinterpreted a prompt.
• ·Budget caps define a maximum amount the agent can spend over a given period, such as one hundred dollars per day or five hundred dollars per week. If the agent encounters a loop, a bug, or a sequence of losing trades, the cap stops the bleeding. The budget cap is enforced by the infrastructure, not by the agent's own logic, so the agent cannot override it.
• ·Position limits prevent concentration. You can specify that no single event may represent more than a set percentage of your allocated capital, or that the agent may not hold more than a fixed dollar amount in any one outcome. This is critical in prediction markets, where a single bad resolution can render a large position worthless.
• ·Exit plans tell the agent when to close a position. The rule might be a calendar date before the event resolves, a probability threshold where the expected value turns negative, or a maximum loss level measured in dollars. Without an exit plan, the agent may hold shares through resolution and accept a binary outcome you never intended to risk fully.
• ·The panic or kill switch lets you flatten every open position and revoke the API key instantly. If news breaks that invalidates your assumptions, or if the agent begins behaving unexpectedly, you can stop all activity without waiting for the agent to process a new chat message.

The non-custodial trading checklist covers how to set these controls in sequence.

How can you test an agent before it risks real money?

Felix includes paper trading so you can observe how the agent behaves without committing capital. You should run the agent in this environment for a meaningful period, watching how it interprets your instructions, how it sizes orders, and whether it respects the scoped limits you configured. Paper trading reveals integration issues, prompt ambiguities, and API misunderstandings before they cost money. It also lets you test the panic switch and verify that revoking the key actually halts activity.

It is important to understand what paper trading can and cannot simulate. It will show you that the agent can place orders, read prices, and manage a virtual portfolio. It will not perfectly replicate the liquidity gaps, slippage, or resolution delays that occur when real money is on the line. In a prediction market, paper trading may fill your order instantly at the midpoint, while a live market might only fill you at a much worse price due to thin order books. Treat paper trading as a functional test, not as a proof of profitability.

When you move to live trading, you must explicitly authorize the key for real money. This is a deliberate step, not a default setting. Start with a small budget that you can afford to lose entirely. Prediction markets are volatile, and event outcomes can be binary. A single resolved market can make a position worth either the full payout or zero. You should not increase the budget until you have seen the agent handle at least one full event cycle, from entry through exit or resolution, without breaching your guardrails.

During this initial live period, review every trade manually. Check whether the agent's reasoning matches the market conditions, whether it is trading too frequently, and whether it is ignoring your exit rules. Look for drift between what you intended and what the agent executed. If you notice discrepancies, tighten the prompt, reduce the scope, or return to paper trading. The run an AI trading agent safely article provides a framework for this monitoring phase.

What are the specific risks of letting an AI agent trade event contracts?

Trading can lose money, including everything you allocate to the agent. Prediction markets carry specific risks beyond general price volatility. Resolution risk means the venue may delay, contest, or misinterpret the outcome. Even if you hold the correct shares, a prolonged resolution process can lock up capital and prevent you from reinvesting elsewhere. In some cases, the final determination may differ from your reading of the facts, or the criteria may be ambiguous enough that the venue chooses an interpretation you did not expect.

Liquidity risk is equally serious. The agent may build a position easily when volume is high, then find no willing counterparty when it tries to exit. As an event approaches resolution, informed traders often leave, and spreads widen. If your agent relies on market orders to exit, it may receive fills at prices that imply a near certain loss. You should consider whether the agent checks order book depth or simply trades at market. In a thin prediction market, liquidity can evaporate in minutes.

Model risk arises from the AI's reasoning. A large language model may conflate correlation with causation, or it may treat a probability of sixty percent as a guaranteed edge without accounting for the payout structure and fees. It may also trade on news that is already fully priced in, or it may misinterpret the event criteria and bet on the wrong outcome entirely. The model does not have intrinsic domain knowledge about every event; it has patterns from text. Those patterns may be misleading when the event is unprecedented or when the market structure differs from historical examples.

Operational risk covers the mechanics of automation itself. API errors, key leakage, or prompt injection could cause unintended orders. Even with scoped keys and budget caps, a determined attacker or a confused agent could burn through the daily limit if you do not monitor logs. You should review every trade, audit the prompts, and rotate keys periodically. No safety feature can eliminate the possibility of loss; it can only bound it. If you are new to automation, the what is agentic trading article defines the core concepts that govern all these interactions.