L3 Abstractions for Agentic Prediction Markets

# Traditional Architecture Attack Surfaces:
# ❌ Open ports (22, 80, 443, 8545...) → Port scanning
# ❌ Public IP addresses → DDoS, geolocation, blocking
# ❌ DNS queries → Traffic analysis, censorship
# ❌ TLS certificates → CA compromise, MITM
# ❌ Service enumeration → Vulnerability discovery

# Subtun L3 Architecture:
# ✅ No listening ports → Nothing to scan
# ✅ Bogon IP addresses → Not routable on public internet
# ✅ P2P name resolution → No DNS leakage
# ✅ Self-certified Ed25519 → No CA dependencies
# ✅ Whitelist-only ACLs → No service enumeration

# Start an agentic oracle node with local GPU inference
sudo subtun \
  --gateway \
  --circuit relay.subfrost.io \
  --register oracle-agent-7f3a \
  -p 8080:8080  # Only expose the consensus API

# The node is now:
# - Reachable only by peers who know its registered name
# - Protected by Noise encryption on all connections
# - Invisible to port scanners (no public listening sockets)
# - Running inference entirely on local GPU (no API calls out)

use subfrost_consensus::{Agent, Market, Resolution, Evidence};
use ollama_rs::Ollama;

pub struct OracleAgent {
    llm: Ollama,
    identity: Ed25519Keypair,
    network: SubP2PProvider,
    reputation_stake: u64,
}

impl Agent for OracleAgent {
    async fn resolve_market(&self, market: &Market) -> Resolution {
        // 1. Gather evidence from multiple sources
        let evidence = self.gather_evidence(&market.query).await;

        // 2. Local LLM inference - no external API calls
        let analysis = self.llm.generate(
            "llama3.1:70b",
            &self.format_resolution_prompt(market, &evidence)
        ).await?;

        // 3. Parse structured resolution
        let resolution = self.parse_resolution(&analysis)?;

        // 4. Create cryptographic attestation
        let attestation = Attestation {
            market_id: market.id,
            resolution: resolution.clone(),
            evidence_hashes: evidence.iter().map(|e| e.hash()).collect(),
            timestamp: SystemTime::now(),
            signature: self.identity.sign(&resolution.to_bytes()),
        };

        // 5. Submit to consensus network
        self.network.broadcast_resolution(attestation).await?;

        resolution
    }

    async fn gather_evidence(&self, query: &str) -> Vec<Evidence> {
        // Autonomous evidence gathering:
        // - Web crawling with local processing
        // - Document analysis (PDFs, filings, etc.)
        // - Social media sentiment (without API dependencies)
        // - Historical data from local indexes

        let mut evidence = Vec::new();

        // Each evidence source runs locally
        evidence.extend(self.crawl_web_sources(query).await);
        evidence.extend(self.analyze_documents(query).await);
        evidence.extend(self.process_historical_data(query).await);

        evidence
    }
}

use subfrost_consensus::{ConsensusRound, Vote, Threshold};

pub struct MeshConsensus {
    local_agent: OracleAgent,
    peer_votes: HashMap<PeerId, Vote>,
    evidence_commitments: HashMap<PeerId, Hash>,
    reputation_weights: HashMap<PeerId, f64>,
}

impl MeshConsensus {
    /// Two-phase commit for market resolution
    pub async fn resolve(&mut self, market: &Market) -> Result<Resolution> {
        // Phase 1: Evidence Commitment
        // All agents commit to their evidence hashes before seeing others
        let my_evidence = self.local_agent.gather_evidence(&market.query).await;
        let commitment = self.commit_evidence(&my_evidence);
        self.broadcast_commitment(commitment).await?;

        // Wait for commitment threshold (2/3 of reputation-weighted agents)
        self.wait_for_commitments(Threshold::TwoThirds).await?;

        // Phase 2: Resolution Voting
        // Now agents reveal their resolutions with evidence
        let my_resolution = self.local_agent.resolve_market(market).await;
        self.broadcast_resolution(my_resolution, my_evidence).await?;

        // Aggregate votes weighted by reputation
        self.wait_for_resolutions(Threshold::TwoThirds).await?;

        let final_resolution = self.aggregate_resolutions()?;

        // Update reputations based on agreement with final outcome
        self.update_reputations(&final_resolution);

        Ok(final_resolution)
    }

    fn aggregate_resolutions(&self) -> Result<Resolution> {
        let mut weighted_votes: HashMap<Resolution, f64> = HashMap::new();

        for (peer_id, vote) in &self.peer_votes {
            let weight = self.reputation_weights.get(peer_id).unwrap_or(&0.0);
            *weighted_votes.entry(vote.resolution.clone()).or_default() += weight;
        }

        // Resolution with >50% of reputation-weighted votes wins
        weighted_votes
            .into_iter()
            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
            .map(|(resolution, _)| resolution)
            .ok_or(ConsensusError::NoMajority)
    }
}

use libp2p_webtransport_sys::WebTransport;
use subp2p::{NetworkProvider, SwarmBuilder};

/// Configure the HTTP/3 transport stack for oracle agents
pub fn create_oracle_transport(
    identity: Keypair,
    relay_addr: Multiaddr,
) -> Result<impl NetworkProvider> {
    let transport = WebTransport::new(identity.clone())
        .with_certificate_verification(CertVerification::SelfSigned)
        .with_noise_encryption(NoiseConfig::XX);

    let swarm = SwarmBuilder::new(identity)
        .with_transport(transport)
        .with_relay_client(relay_addr)
        .with_kademlia()      // Peer discovery
        .with_gossipsub()     // Vote broadcasting
        .with_dcutr()         // NAT hole punching
        .build()?;

    Ok(swarm)
}

use ollama_rs::{Ollama, GenerationRequest};

pub struct LocalInferenceEngine {
    ollama: Ollama,
    model: String,
    gpu_layers: u32,
}

impl LocalInferenceEngine {
    pub fn new(model: &str) -> Self {
        // Connect to local Ollama instance (no external network)
        let ollama = Ollama::new("http://127.0.0.1:11434".into());

        Self {
            ollama,
            model: model.to_string(),
            gpu_layers: 35, // Offload to GPU for performance
        }
    }

    pub async fn analyze_evidence(
        &self,
        market_query: &str,
        evidence: &[Evidence],
    ) -> Result<Analysis> {
        let prompt = format!(
            r#"You are an oracle agent resolving a prediction market.

MARKET QUERY: {market_query}

EVIDENCE:
{evidence_text}

Analyze the evidence and determine:
1. Whether the market condition has been met (YES/NO/UNCERTAIN)
2. Confidence level (0.0 to 1.0)
3. Key evidence supporting your conclusion
4. Any conflicting evidence or uncertainties

Respond in JSON format."#,
            market_query = market_query,
            evidence_text = self.format_evidence(evidence),
        );

        let response = self.ollama
            .generate(GenerationRequest::new(self.model.clone(), prompt))
            .await?;

        self.parse_analysis(&response.response)
    }
}

use subfrost_markets::{ConditionalMarket, Outcome, Position};

/// A conditional market with agentic AI resolution
pub struct AgenticMarket {
    pub id: MarketId,
    pub query: String,
    pub resolution_criteria: ResolutionCriteria,
    pub resolution_deadline: SystemTime,
    pub oracle_network: MeshConsensus,
    pub positions: HashMap<PeerId, Position>,
}

impl AgenticMarket {
    /// Create a new conditional market
    pub async fn create(
        query: &str,
        criteria: ResolutionCriteria,
        deadline: SystemTime,
        network: &SubP2PProvider,
    ) -> Result<Self> {
        let market = Self {
            id: MarketId::generate(),
            query: query.to_string(),
            resolution_criteria: criteria,
            resolution_deadline: deadline,
            oracle_network: MeshConsensus::new(network.clone()),
            positions: HashMap::new(),
        };

        // Broadcast market creation to the network
        network.broadcast_message(
            P2pMessage::MarketCreated(market.to_announcement()),
            "markets"
        ).await?;

        Ok(market)
    }

    /// Take a position in the market
    pub async fn take_position(
        &mut self,
        trader: PeerId,
        outcome: Outcome,
        amount: u64,
    ) -> Result<Position> {
        // Verify trader has sufficient collateral
        // Create position record
        // Update market state
        // Broadcast position to network

        let position = Position {
            trader,
            outcome,
            amount,
            timestamp: SystemTime::now(),
        };

        self.positions.insert(trader, position.clone());
        Ok(position)
    }

    /// Resolve the market using agentic consensus
    pub async fn resolve(&mut self) -> Result<Resolution> {
        // Ensure deadline has passed
        if SystemTime::now() < self.resolution_deadline {
            return Err(MarketError::DeadlineNotReached);
        }

        // Initiate mesh consensus resolution
        let resolution = self.oracle_network.resolve(&self.into()).await?;

        // Distribute payouts based on resolution
        self.distribute_payouts(&resolution).await?;

        Ok(resolution)
    }
}

// Example: Create a conditional market
async fn example_market(network: &SubP2PProvider) -> Result<()> {
    let market = AgenticMarket::create(
        "Will the Federal Reserve cut interest rates by 50bps at the December 2024 meeting?",
        ResolutionCriteria {
            evidence_sources: vec![
                "Federal Reserve press releases",
                "FOMC meeting minutes",
                "Official Fed announcements",
            ],
            resolution_type: ResolutionType::Binary,
        },
        SystemTime::now() + Duration::from_days(30),
        network,
    ).await?;

    println!("Created market: {}", market.id);
    Ok(())
}