Mochifi
Futarchy analyst
About
The problem it solves # 🛡️ The Problem It Solves Demo: https://share.descript.com/view/pnsbObRikUV ### The Challenge: Information Chaos & Adversarial Markets Traders and participants in futarchic decision markets (such as **Umia**, **MetaDAO**, and **Polymarket**) operate in a high-stakes, high-noise environment. Currently, making an informed decision requires manually tracking scattered signals across Telegram, X (Twitter), Discord, GitHub activity, and on-chain movements. This fragmented landscape leads to critical failures: * **High Cognitive Load:** Participants spend hours cross-referencing data, leading to slow and inefficient decision-making. * **Vulnerability to Manipulation:** Markets are often swayed by coordinated misinformation and "pump-and-dump" schemes disguised as community consensus. * **Inefficient Capital Allocation:** Poor information quality results in mispriced risks in high-stakes governance and prediction markets. --- ### The Solution: Umia Analyst Agent The **Umia Analyst Agent** is an autonomous AI research analyst designed to turn chaotic data into actionable, auditable intelligence. It operates as a continuous intelligence layer that: * **Smart Ingestion & Weighting:** Automatically classifies signals from multiple sources using a credibility-weighted model (**Verifiable** / **Partial** / **Manipulable**). * **Adversarial Synthesis:** Generates structured Bull and Bear cases with direct citations, ensuring every claim is backed by evidence. * **Anti-Manipulation Shield:** Actively flags coordinated manipulation attempts by cross-referencing social hype with on-chain reality. * **Skin in the Game:** Beyond mere reporting, the agent publishes structured trading memos, takes its own positions, and maintains a public, transparent PnL track record. --- ### 🚀 Key Benefits | **For Traders** | **For Protocols** | **For the Ecosystem** | | :--- | :--- | :--- | | Reduces research time from hours to seconds. | Improves the quality of governance decisions. | Enhances market efficiency and liquidity. | | Minimizes exposure to social engineering. | Attracts informed capital via clear reporting. | Reduces the success rate of malicious actors. | | Provides an auditable trail of logic for every trade. | Hardens the futarchy mechanism against "noise." | Sets a new standard for AI-driven financial transparency. | **The Umia Analyst Agent doesn't just provide data—it provides the "Signal" necessary to navigate the "Noise" of decentralized governance.**
How it's made
Challenges we ran into # Challenges WE Ran Into Building the **Umia Analyst Agent** presented several unique technical and conceptual hurdles. Our goal was to move beyond simple "AI wrappers" and build a resilient, institutional-grade engineering system. ### 1. Adversarial Information Filtering The biggest challenge was creating a system that doesn't just aggregate data but actively resists manipulation. In futarchic markets, we found that social signals (Telegram/X) are often heavily manipulated by proposal authors to create "fake consensus." * **The Solution:** We implemented a **Source Classification Layer**. We assigned deterministic weights to data: verifiable sources (on-chain transactions, GitHub commits) are weighted at `1.0`, while manipulable social channels are strictly capped at `0.3`. ### 2. Ensuring Agentic Reliability Standard LLM prompts often suffer from "hallucinations" or "groupthink." In a financial context, we couldn't risk the agent making biased or "hallucinated" trading recommendations. * **The Solution:** We utilized a **LangGraph-based multi-node architecture**. By separating the pipeline into distinct stages—Source Classification, Synthesis, and Position-taking—and adding an autonomous **Self-Reviewer** node (the "Advocatus Diaboli"), we forced the agent to argue against its own conclusions before finalizing a memo. ### 3. Domain Shift & Architecture Repurposing We started with a robust pipeline designed for corporate credit underwriting and had to transform it into a DeFi intelligence tool within 48 hours. Mapping "banking risk" to "governance risk" required a total overhaul of the business logic. * **The Solution:** We maintained the underlying "plumbing" (deterministic logic gates, audit trails, and versioned skills) while completely swapping the domain layers. This allowed us to preserve institutional-grade reliability while moving at hackathon speed. ### 4. Data Ingestion Latency Scraping decentralized forums and GitHub activity in real-time is computationally expensive and prone to rate limits. * **The Solution:** We developed a dual-mode ingestion system. For the demo, we use a high-fidelity mock-market layer based on historical MetaDAO proposals, while the production environment is designed to utilize Apify actors for fresh signal ingestion. ### 5. Ecosystem Early-Stage Adaptation (The MetaDAO Bridge) Since Umia’s native decision markets are currently in their earliest stages, we faced a "cold start" problem regarding live data for testing our agent's predictive accuracy. * **The Solution:** We chose to stress-test our agent on **MetaDAO** (the pioneer of futarchy). This is a strategic advantage: by proving our agent can navigate the most adversarial and "noisy" established market in the space, we ensure it will be the most robust and battle-tested analyst the moment Umia’s mainnet markets go live. We aren't just building for a sandbox; we are building for the open ocean.