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 } ] }

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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)

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