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)"
Why This Matters for Production
When your agent pipeline runs 100+ times per day, "check the logs" doesn't scale. You need:
- Proactive alerts (not reactive grep)
- Structured traces (not raw text)
- Per-agent metrics (not aggregate "it works")
We built AgentForge because nothing else gave us this.
https://github.com/agentforge-cyber/agentforge-mvp
How do you monitor your agent systems today? Raw logs or structured traces?
Posted on 2026-06-15 by the AgentForge team.