Real-Time Monitoring for AI Agents: Beyond Log Streaming

The AgentForge team built an open-source monitoring stack for AI agents that goes beyond traditional log streaming, offering live execution views, state inspection, and per-agent performance metrics. The system provides structured JSON execution traces, a real-time WebSocket dashboard with active agent heartbeats and error rate tracking, and proactive alert rules. AgentForge was created because existing log-based monitoring fails to scale for production pipelines running over 100 times per day.

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: 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 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 https://github.com/agentforge-cyber/agentforge-mvp How do you monitor your agent systems today? Raw logs or structured traces? Posted on 2026-06-05 by the AgentForge team.