# Roadmap Overview

Yala’s development follows a staged roadmap, with each phase expanding intelligence, validation, and system scope. This staged approach allows Yala to maintain continuous public value output and narrative relevance with minimal initial system complexity.

### Early Stage: Establishing the First Fair-Value Agent

In the early stage:

* Yala conducts closed, internal testing of its first fair-value AI agent
* A steady stream of probability estimates is released publicly via Yala’s official X account
* The focus is on methodology, calibration, consistency, and probabilistic reasoning

These early outputs demonstrate how Yala approaches fair-value estimation and lay the foundation for more advanced capabilities.

### Mid Stage: Public Launch of the Fair-Value AI Agent

In the mid stage, Yala publicly launches its first fair-value AI agent.

**Agent Scope**

* Designed for price-prediction markets and risk-neutral valuation
* Operates as a verifiable, measurable system
* Performance is continuously tested in real markets

**Primary Signal Sources**

* Historical trading data
* News-driven event analysis
* Smart-money tagging information
* Social-media-based sentiment dynamics

**User Inputs**

Users interact with the agent via a structured input format:

* Market type (sports events or crypto markets)
* Target condition (price, direction, or range)
* Time horizon (future timestamp)

**Outputs**

* A probability estimate representing fair value for the specified condition

**Live Validation**

* The agent operates in a controlled live environment
* Manages approximately $1,000–$10,000 in capital
* Executes trades autonomously. In validation environments, probability signals are translated into position-sizing decisions using predefined deviation thresholds between fair value and market-implied probabilities.
* Validates fair-value logic under real-world conditions with strict risk limits

### Late Stage: Multi-Agent Fair-Value System

In the late stage, Yala evolves into a comprehensive multi-agent fair-value system capable of generating explainable probability assessments across markets, signals, and event types.

The system expands into a coordinated swarm architecture, where a Supervisor (Orchestrator) Agent coordinates specialized Worker Agents to produce multi-factor fair-value outputs that integrate both risk-neutral and subjective probabilities.

**Expanded Scope and Outputs**

Users can submit queries across any asset or event category, including crypto, equities, elections, esports, and macro outcomes, along with a future time horizon.

The system generates:

* Probability density functions (PDFs)
* Multi-factor fair-value curves
* Confidence intervals
* Distribution shapes

These outputs provide a holistic probabilistic view beyond single-point estimates.

**Key Capability Expansions**

Compared with earlier stages, the late-stage system introduces:

* Subjective fair value alongside risk-neutral pricing, incorporating sentiment, macro, and contextual signals
* Multi-market coverage across the full prediction-market landscape
* Multi-factor intelligence, integrating options data, market sentiment, ETF flows, and macro events
* Multi-agent swarm architecture, enabling parallel analysis and coordinated signal aggregation

**Supervisor and Worker Agents**

The Supervisor (Orchestrator) Agent manages query parsing, task assignment, execution monitoring, and final aggregation.

Worker Agents include fair-value modeling, data collection, sentiment analysis, smart-money analysis, event tracking, options analysis, simulation, and decision aggregation agents.

**Advanced Capabilities**

The late stage also introduces private-information adjustment, encrypted data handling, strategy-gated prediction vaults, autonomous capital allocation, and tokenized agent monetization.

By this stage, Yala functions as a fair-value operating system supporting agent-driven forecasting, coordination, and prediction-market infrastructure at scale.

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