yevhens-hue ⚡ Signal-Based Autonomous AI Agent Architecture
Production framework for building reactive, event-driven AI agents that execute multi-step workflows based on incoming telemetry, market signals, lead actions, and webhook events.
🏛️ System Architecture
┌─────────────────────────────────────────────────────────────────────────────┐
│ ⚡ SIGNAL-DRIVEN AGENT ENGINE (Python / FastAPI / Redis / LangGraph) │
├─────────────────────────────────────────────────────────────────────────────┤
│ 1. SIGNAL INGESTION & EVENT BUS: │
│ • Webhook Triggers: Stripe charge, Intercom conversation, Form submit │
│ • Polling / Streaming: RSS Feeds, Market Tickers, Video Open Telemetry │
│ • Idempotent Message Ingestion (Deduplication via SHA-256 + Redis Lock) │
├─────────────────────────────────────────────────────────────────────────────┤
│ 2. SIGNAL EVALUATOR & CLASSIFICATION (Fast / Deterministic): │
│ • Fast Filter (Claude 3.5 Haiku / Rule Engine): │
│ - Priority Score (0-100) │
│ - Intent Type: {CHURN_RISK, BUYING_SIGNAL, SUPPORT_ESCALATION, FRAUD} │
├─────────────────────────────────────────────────────────────────────────────┤
│ 3. AGENTIC STATE MACHINE & TOOL EXECUTION: │
│ • LangGraph State Graph with Dynamic Branching │
│ • Safe Tool Invocation with Rate Limiting & Schema Validation │
│ • Human-in-the-Loop (HITL) Gate for High-Risk Actions (> $500 impact) │
├─────────────────────────────────────────────────────────────────────────────┤
│ 4. OBSERVABILITY & AUDIT TRAIL: │
│ • Full Telemetry: Latency, Token Usage, Tool Call Diffs │
│ • Real-Time Dispatch: Slack Alerts, Telegram Digest, CRM Update │
└─────────────────────────────────────────────────────────────────────────────┘
💻 Python Reference Implementation (LangGraph + Redis)
from typing import Dict, Any, TypedDict
from langgraph.graph import StateGraph, END
import asyncio
import logging
logger = logging.getLogger("signal-agent")
class SignalState(TypedDict):
signal_id: str
event_type: str
payload: Dict[str, Any]
priority_score: int
intent: str
action_taken: str
requires_human_review: bool
def classify_signal(state: SignalState) -> SignalState:
event = state["event_type"]
payload = state["payload"]
# Deterministic rule + LLM extraction
if event == "high_value_video_view":
state["priority_score"] = 95
state["intent"] = "HOT_LEAD_ENGAGEMENT"
elif event == "payment_failed":
state["priority_score"] = 80
state["intent"] = "CHURN_PREVENTION"
else:
state["priority_score"] = 40
state["intent"] = "GENERAL_NOTIFICATION"
state["requires_human_review"] = state["priority_score"] > 90
return state
def execute_action(state: SignalState) -> SignalState:
intent = state["intent"]
if intent == "HOT_LEAD_ENGAGEMENT":
# Trigger real-time sales alert & schedule follow-up
state["action_taken"] = "DISPATCHED_INSTANT_TELEGRAM_AND_CRM_LEAD_STAGE_UPDATE"
elif intent == "CHURN_PREVENTION":
state["action_taken"] = "SENT_GRACEFUL_CARD_UPDATE_SMS_AND_EMAIL"
else:
state["action_taken"] = "LOGGED_TO_ANALYTICS_EVENT_STREAM"
return state
# Compile State Graph
workflow = StateGraph(SignalState)
workflow.add_node("classify_signal", classify_signal)
workflow.add_node("execute_action", execute_action)
workflow.set_entry_point("classify_signal")
workflow.add_edge("classify_signal", "execute_action")
workflow.add_edge("execute_action", END)
signal_agent_engine = workflow.compile()
📌 Standard Proposal Framework for Signal & Event Agent Jobs ($40–$65/hr)
- Milestone 1: $100 Paid Discovery & Architecture Prototype
- Deliverable: Signal ingestion endpoint + LangGraph classification state machine proof ($100.00).
- Milestone 2: Multi-Source Webhook Connectors & Tool Integration ($45.00/hr)
- Milestone 3: Observability Dashboard, Sentry Alerts & Handover ($45.00/hr)