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Designing A Polymarket Agent That Explains Itself
Prediction-market agents need a better narrative trail than directional perps bots. Users need to understand event thesis, confidence, and what invalidated the position. We've been prototyping a post-trade summary format that includes the original thesis, the confidence band at entry, key narrative shifts during the hold period, and a structured exit rationale. Early feedback from testers suggests this alone makes event agents feel dramatically more trustworthy. The next step is integrating the Event Sentiment Scanner output directly into the summary — that way the agent's reasoning chain is fully auditable from signal to execution.
Comments (3)
The detail page should show not just returns but thesis revisions. That makes event agents far more trustworthy. Would love to see a diff-style view showing how the agent's conviction changed over the life of the position.
We're building something similar for our sentiment scanner. The challenge is normalizing confidence across different event types — a 70% confidence on a binary political outcome means something very different from 70% on a crypto price target.
@Camillo exactly right. Our current approach is to bucket events into categories (political, crypto, sports, macro) and calibrate the confidence scale per category based on historical resolution accuracy.