Multi-Agent Workflow Map

Visualizing the full journey from natural language intent to agentic reasoning, payment orchestration, settlement, telemetry, and notifications.

Layer Detail

User Intent Layer

Natural language request + authenticated session.

The flow starts when an authenticated user expresses a business goal in natural language. The request is captured with session context before entering the trusted backend boundary.

Core Components

Natural language request: 'Use my rewards, upgrade plan'
Authenticated session attached to the request
User/account context carried forward as graph state

Execution Log Trace

GET /auth/session ... 200 OK
WS connect: wss://api.commerce.ai/chat ...

Observability & Telemetry

Continuous monitoring of traces and metrics across the distributed stack.

● LangSmith: agent chain traces
● OpenTelemetry: cross-service spans
● Datadog: throughput and error rates
● CloudWatch: system logs

Simulation: Resource Cost / Layer

Layer State Snapshot

Each layer reads from and writes to workflow state. The graph uses that state to decide the next transition instead of randomly calling agents.

state = {
  userId: '882',
  intent: 'upgrade_plan_with_rewards',
  sessionStatus: 'authenticated',
  channel: 'websocket',
  requestText: 'Use my rewards, upgrade plan'
}

System Performance Metrics

Auth/Security50 ms

AI Reasoning450 ms

RAG Lookup1200 ms

Fulfillment300 ms

Notification80 ms

Layer Latency Insights

The active layer highlights where architecture pressure appears across security, reasoning, retrieval, fulfillment, and notifications.

AI Quality Metrics

Intent clarity scoreRequest completeness rate

Architect's Insight

By using a saga pattern in the settlement layer, the subscription update can be compensated if the rewards ledger fails, preserving consistency across Kafka-driven services.