# Real-Time Monitoring for AI Agents: Beyond Log Streaming

> Source: <https://dev.to/albert_zhang_f468830cf0e6/real-time-monitoring-for-ai-agents-beyond-log-streaming-nin>
> Published: 2026-06-05 11:00:12+00:00

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?
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**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.*