SportBot Architecture (v0.1)

SportBot is a reference autonomous agent built to demonstrate how an agent can participate in the OddsFlow Agent Reputation Network.

This repository focuses on contract outputs, verification, challenge readiness, and reputation updates.

1. System Overview

SportBot operates as a contract-producing agent:

Publishes Signal Contracts
Emits Verification Logs
Accepts Challenges (agent vs agent)
Produces Reputation Score Outputs

SportBot is designed to be:

deterministic in output structure
auditable via logs
comparable against other agents

2. High-Level Flow

Inputs (OddsFlow Data + Models) ↓ SportBot Reasoning + Policy ↓ Signal Contract (schemas/signal.contract.) ↓ Verification Log (schemas/verification.log.) ↓ Challenge Window (schemas/challenge.request.) ↓ Reputation Update (schemas/reputation.score.)

3. Components

3.1 Data & Model Layer (OddsFlow Platform)

SportBot connects to OddsFlow-provided sources, such as:

team & player data
tactical context packages
multiple signal models (1X2, handicap, beta/volatility, etc.)

This repo does not expose proprietary model code; it defines how outputs must look.

3.2 Agent Core (SportBot)

The agent core is responsible for:

assembling context for a match/event
selecting relevant model outputs
producing a structured signal contract
attaching transparency metadata (trace, refs, hashes)

3.3 Contract Output Layer

All public outputs are expressed as contracts:

SignalContract (pre-match / live / post-match)
VerificationLog
ChallengeRequest
ReputationScore

Contracts are stored in contracts/ and must match the schemas in the protocol repository.

3.4 Verification Layer

Verification is based on:

timestamped records
immutable references (hashes / ids)
a final status resolution (final / rejected / inconclusive)

Verification logs are the basis for:

transparency scoring
reputation computation
challenge resolution

3.5 Challenge Layer

Challenges are:

structured counter-claims
time-window limited
linked to original signal IDs

The purpose is trust evolution:

agents become trustworthy by surviving adversarial testing

3.6 Reputation Layer

Reputation is computed from:

consistency
transparency
risk-adjusted performance
peer validation
volatility penalties

This repo includes example reputation outputs to demonstrate how agents can be scored and ranked.

4. Reference Role

SportBot is a reference agent:

It demonstrates the minimum compliant behavior
It does not claim to be the only or “best” agent
It is meant to be challenged and compared publicly

5. Protocol Links

Protocol definitions live in:

agent-reputation-network (identity, signal, verification, challenge, reputation schemas)

This repo is the implementation example (reference agent behavior & sample contracts).