AI agents can trade prediction markets when a developer connects them to live market data, contract rules, a trading API, and risk controls. The agent researches an event and proposes a trade. Code outside the model checks the contract, executable price, fees, size, and position limits before any order goes out. Kairos supplies market data and execution across supported venues through one API.
A trader testing an agent on Kalshi described the early result on Reddit: "Just an experiment at this point, it hasn't won anything lol." The useful question is whether the agent can follow a repeatable process after the first promising forecast.
What does an AI prediction market trading agent need?
Start with one event category and a source you trust. Give the agent the exact market question, its resolution rules, closing time, current bids and asks, available depth, and the evidence it may cite. An agent that sees only a headline and a last-traded price has too little information to propose a real order.
- A research step that returns a probability estimate, evidence links, and a reason to pass.
- A market-matching step that identifies the exact outcome token and checks whether similar contracts settle on the same terms.
- An execution step that reads the live quote, gets a fee quote, applies limits, submits the order, and records the fill.
The Kairos API exposes market discovery, live data, and authenticated order entry across supported venues. Its unified market IDs reduce the work of reconciling venue-specific symbols. You still own the strategy and the decision rules.
How should the agent turn a forecast into a trade?
Make the agent return structured output: market ID, outcome, estimated probability, supporting sources, maximum acceptable price, proposed size, and an expiration time for its recommendation. Reject incomplete output. A free-form paragraph is a research note. The order service needs structured fields.
Next, calculate the gap between the agent's estimate and the price you can fill. Use the ask for a buy and the bid for a sale. Check depth at the proposed size. Subtract venue and platform fees before deciding whether the gap survives. If two venues list similar events, compare their resolution rules before comparing prices. A one-word difference in a contract can erase the apparent edge.
Only then pass the candidate to a deterministic order service. Kairos order documentation requires an exact outcome and a price on every order, including market orders. That price acts as the worst acceptable boundary for the trade. The execution system should reject an order when the quote moves past that boundary.
Which controls belong outside the model?
The model should never be the final authority on position size, duplicate orders, or whether an order filled. A trader building an AI agent described the failure mode on Reddit: "an API call times out but the order actually went through" Retrying blindly can open a second position.
- Set maximum exposure per market and per day in code the model cannot change.
- Use scoped credentials. Kairos separates trade execution, trade history, and position access, and requires authentication for every execution endpoint.
- Attach a client order ID so retries do not create duplicate orders.
- Read order and fill events back from the venue, then reconcile them against local state.
- Stop new orders when market data is stale, the venue disconnects, or position records disagree.
Kairos documents scoped API keys and IP restrictions, fee quotes before submission, order IDs for retry protection, and live order and fill events. Those are useful building blocks. The developer still has to set the limits, monitor the system, and decide when human approval is required.
How do you test an agent before live trading?
Run the research step without execution first. Save every recommendation with the market snapshot and resolution rules it used. Compare its probability estimates with outcomes and with a simple baseline that never calls a model. This exposes weak market selection before a live order hides the mistake behind a fill.
Then run paper trades using the price and depth available at the time of the signal, including fees. A paper order filled at a midpoint that was never available will overstate performance. Move to small live size only after the order service handles rejected orders, partial fills, restarts, and stale quotes. Review the trade log after every session.
An agent does not need to trade every signal. A clear PASS decision when the contract is ambiguous or the book is thin is a successful output. Real capital waits behind the risk checks.
Where does Kairos fit in the agent stack?
Kairos is the market data and execution layer. A developer can read and trade supported venues through one REST and WebSocket integration. That reduces the work of maintaining separate venue connectors. The API covers Polymarket, Kalshi, Predict.fun, and Hyperliquid according to the current product documentation.
The agent supplies the thesis. Your code decides whether the thesis is allowed to become an order. Kairos carries that order to a supported venue and reports what happened. Traders who want to inspect the same markets by hand can use the Kairos terminal. For the research side, read AI trading in prediction markets.