How to build an AI agent for prediction markets

Build a prediction-market agent around a simple loop: fetch the attention queue, inspect the top market, ask why it moved, check resolution risk, summarize the evidence, and create an alert or brief.

The core design choice is discipline. The agent should not scrape random pages or infer hidden information. It should call typed endpoints, preserve timestamps, cite sources, and refuse trading instructions.

Keep the system in layers. The data layer fetches market state. The decision layer ranks attention and risk. The payment layer handles x402 challenges. The LLM layer turns structured evidence into a short grounded explanation.

This separation makes failures easier to debug. If the LLM output is wrong, you can check whether the data, prompt, or source rule failed.

• Scheduler: decides when to run.
• Fetcher: calls health, attention, why, and resolution-risk endpoints.
• Budget guard: rejects calls above a per-run limit.
• LLM summarizer: writes from supplied JSON only.
• Notifier: sends alert, brief, webhook, or human handoff.

The system prompt should tell the model to stay inside supplied data, cite timestamps, avoid outcome predictions, flag uncertainty, and use routing actions instead of trading verbs.

A compact response format helps: headline, what changed, evidence, risks, what to verify, next call.

Before running the agent on a schedule, test the health endpoint, force a 402 challenge, verify payer replay, set a budget cap, and run one dry brief to confirm the model does not turn evidence into advice.

Once deployed, monitor failures separately: upstream data errors, payment errors, LLM errors, and delivery errors should not collapse into one generic exception.