Real-Time Monitoring for AI Agents: Beyond Log Streaming

Most agent monitoring is "log everything and grep later." That's not monitoring — that's archaeology. 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 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} ] } 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) alerts: - condition: "agent.error_rate > 0.1" action: "circuit_breaker.open(agent)" - condition: "pipeline.latency > 30000" action: "pagerduty.notify(critical)" 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.