RiverGen DIA Source Patterns (1.0.2)

Page Outline

• Implementation Patterns 1.0.2
  • High-Velocity Telemetry Ingestion (Kafka) — rivergen_ingestor.py
  • Autonomous Decision Controller — controller_102.py
  • Intelligence Simulation Core — simulator_v102.py
• 1.0.2 Integration Standards

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Implementation Patterns 1.0.2

The following patterns represent the core engineering standards for the RiverGen Decision Intelligence Agent (DIA) v1.0.2. These snippets demonstrate the transition from general ML logic to the project's autonomous event-driven architecture.

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High-Velocity Telemetry Ingestion (Kafka) — rivergen_ingestor.py

This client is optimized for the RiverGen 1.0.2 telemetry stream, capturing multi-agent signals with low overhead.

# RiverGen Core Implementation v1.0.2
from aiokafka import AIOKafkaConsumer
import asyncio
import json
import logging

logger = logging.getLogger("rivergen.dia.ingestor")

class RiverGenTelemetryClient:
    """
    Asynchronous telemetry consumer for RiverGen Intelligence.
    Handles signals from Model Studio (MSA), Prompt Studio (PSA), and Governance (GA).
    """
    
    def __init__(self, cluster_nodes: list[str]):
        self.nodes = cluster_nodes
        self.consumer = None
        self.handlers = {}
        
    async def start_102_stream(self):
        """Starts the 1.0.2 production telemetry stream."""
        self.consumer = AIOKafkaConsumer(
            "rg.agent.metadata.v102", 
            "rg.governance.alerts.v102",
            bootstrap_servers=self.nodes,
            group_id="rg-dia-brain-102",
            value_deserializer=lambda m: json.loads(m.decode('utf-8'))
        )
        await self.consumer.start()
        logger.info("RiverGen 1.0.2 Telemetry Ingestor: Online")
        
        async for msg in self.consumer:
            await self._dispatch_to_brain(msg.topic, msg.value)

    async def _dispatch_to_brain(self, topic, payload):
        """Routes telemetry to the DIA decision engine."""
        handler = self.handlers.get(topic)
        if handler:
            await handler(payload)

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Autonomous Decision Controller — controller_102.py

The FastAPI controller for the Intelligence Brain manages the Predict-Refine-Act loop.

# DIA Decision Controller 1.0.2
from fastapi import FastAPI, BackgroundTasks, HTTPException
from app.core.brain import DecisionBrain
from app.infra.ga_sidecar import GovernanceClient

app = FastAPI(title="RiverGen Intelligence Brain", version="1.0.2")

# Core Intelligence Services
brain = DecisionBrain(registry_version="1.0.2")
ga_sidecar = GovernanceClient(url="http://ga-agent:8080")

@app.post("/api/decision-intelligence/decide")
async def execute_autonomous_action(req: DecisionRequest):
    """
    RiverGen 1.0.2 Decision Gateway.
    1. Ingests raw agent metadata.
    2. Executes ML-based intent prediction.
    3. Cross-references GA Governance Guidelines.
    """
    # Phase 2: ML Scored Intent
    ml_prediction = await brain.predict_intent(req.agent_metadata)
    
    # Phase 3: Governance Refinement
    policy_check = await ga_sidecar.validate_action(ml_prediction.action)
    
    if not policy_check.is_allowed:
        return {"status": "policy_override", "action": policy_check.suggested_fallback}
        
    return {
        "status": "authorized",
        "action": ml_prediction.action,
        "confidence": ml_prediction.score,
        "rg_version": "1.0.2"
    }

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Intelligence Simulation Core — simulator_v102.py

The simulator executes "What-If" scenarios using the RiverGen Spatial Engine.

# RiverGen Spatial Simulator 1.0.2
class RiverGenSimulator:
    """
    Orchestrates spatial simulations for agent performance forecasting.
    Optimized for the 1.0.2 coordinate mapping system.
    """
    
    async def run_scenario_102(self, scenario_id: str, parameters: dict):
        """
        Executes a spatial simulation across the virtual river segment.
        Returns impact metrics for platform stability.
        """
        # Load the 1.0.2 baseline state
        state = await self.load_baseline("v1.0.2")
        
        # Apply hypothetical parameters
        forecast = await self.spatial_engine.simulate(state, parameters)
        
        return {
            "scenario": scenario_id,
            "latency_delta": forecast.metrics.latency,
            "accuracy_score": forecast.metrics.accuracy,
            "timestamp": "2026-01-30T03:00:00Z"
        }

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1.0.2 Integration Standards

Pattern	Requirement	RiverGen Standard
Concurrency	Async/Await	All DIA I/O must be non-blocking.
Serialization	JSON (UTF-8)	Standard format for multi-agent message passing.
Auditability	Decision Ledger	Every 1.0.2 decision must be logged with its reasoning string.
Versioning	Semantic Locking	Requests without a 1.0.2 header are logged as deprecated.

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• Full Implementation Overview
• Sidecar Components
• Database Integration

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Generated for RiverGen 1.0.2 Technical Integration.