Real-Time Monitoring for AI Agents: Beyond Log Streaming

#ai #monitoring #observability

Most agent monitoring is "log everything and grep later." That's not monitoring — that's archaeology.

What We Actually Need

Live execution view — Which agent is running right now?
State inspection — What data is Agent C holding?
Failure forensics — Why did Agent B timeout? What were its inputs?
Performance metrics — Per-agent latency, token usage, error rate

AgentForge's Monitoring Stack

Execution Trace (Structured JSON)

Every pipeline run generates a trace:

{
  "run_id": "uuid",
  "status": "completed",
  "agents": [
    {"name": "data_fetch", "status": "ok", "latency_ms": 1200, "tokens": 450},
    {"name": "analyzer", "status": "ok", "latency_ms": 3400, "tokens": 2100},
    {"name": "reporter", "status": "ok", "latency_ms": 890, "tokens": 1200}
  ]
}

WebSocket Dashboard

Real-time WebSocket feed showing:

Active agents (with heartbeat)
Queue depth per agent
Error rate (1-min sliding window)
Cost per run (token usage × model price)

Alert Rules

alerts:
  - condition: "agent.error_rate > 0.1"
    action: "circuit_breaker.open(agent)"
  - condition: "pipeline.latency > 30000"
    action: "pagerduty.notify(critical)"