I’ve built what I thought was a clever multi-agent system using Latenode for our customer support process. I have different AI agents handling initial triage, technical troubleshooting, billing questions, and escalation to human agents.
The problem is that during peak times, I have no clear visibility into what each agent is doing, where bottlenecks are forming, or why certain customer interactions are getting stuck.
I’ve heard Latenode has some visual monitoring capabilities for multi-agent systems, but I can’t figure out how to set them up effectively. When we’re handling hundreds of customer inquiries simultaneously, I need a real-time dashboard that shows the status of each agent and the flow of tasks between them.
Has anyone successfully implemented monitoring for complex multi-agent workflows? What’s your approach to debugging when things go wrong across multiple AI agents working together?
I had the exact same problem with our multi-agent customer service system. The solution was to properly configure Latenode’s visual monitoring dashboard.
First, make sure you’re using named nodes for each key step in your agent workflows. Generic names like “Process Data” won’t help you track things - use specific names like “Technical-Agent-Classify-Issue” or “Billing-Agent-Verify-Account”.
Then set up execution history tracking with the detailed logging option enabled. This creates timestamps for each step so you can identify slowdowns.
The game changer for us was adding status update nodes throughout each agent’s workflow. These nodes simply update a central status tracker with their current state and any relevant metrics. We display this in the visual builder’s monitoring view.
For debugging specific issues, use the restart from history feature. When something goes wrong, you can replay that exact scenario to see where it failed.
The visual builder actually shows you the data flowing between agents in real-time when you’re in debug mode. This is invaluable for spotting communication breakdowns.
I ran into this exact problem with our multi-agent setup. Here’s what worked for us:
We implemented a central logging system where each agent reports its status, current task, and any issues at each major step. This gives us a complete timeline of the customer journey across all agents.
We added performance metrics tracking to identify bottlenecks. Each agent reports its processing time for different types of tasks, which helps us spot when an agent is overloaded or underperforming.
We created a visual dashboard that shows the current state of all agents, how many tasks each is handling, and where customers are in their journey. This uses a simple traffic light system (green/yellow/red) to highlight issues.
The most useful thing was implementing “breadcrumb” metadata that follows the customer through each agent interaction. This lets us trace the complete path and see exactly where things went wrong when debugging issues.
I solved a similar multi-agent monitoring challenge for our customer service system. The approach that worked best for us was implementing a centralized event bus architecture.
Each AI agent publishes standardized events to the bus for every significant action it takes - receiving a request, completing analysis, transferring to another agent, encountering an error, etc. These events include all relevant context and timestamps.
A dedicated monitoring service subscribes to this event stream and builds real-time visualizations showing:
The current status and workload of each agent
The flow of requests through the system
Average processing times for each step
Any bottlenecks or errors
For debugging specific issues, we implemented trace IDs that follow each customer request through the entire system. When something goes wrong, we can filter the logs by that trace ID and see exactly what happened across all agents involved in that particular interaction.
I’ve built and monitored multiple AI agent systems for large-scale customer operations. Here’s what works:
First, implement a standardized logging protocol across all agents. Each agent should log key events with consistent metadata: timestamp, agent ID, action type, input summary, output summary, processing time, and a unique session ID that follows the customer across all agents.
Next, build a real-time monitoring dashboard with three views:
System-level metrics showing overall throughput, error rates, and average resolution times
Agent-level metrics showing the current workload, queue depth, and performance of each AI agent
Session-level tracing that lets you follow specific customer journeys across multiple agents
For effective debugging, implement a replay capability that can reconstruct the exact state and inputs that led to a failure. This is essential for reproducing and fixing issues in complex multi-agent systems.
Finally, set up automated alerts for anomalies like unusual processing times, error rate spikes, or communication failures between agents.
we solved this by adding status update nodes in each agent workflow. they all report to a central dashboard. also added unique IDs that follow each customer request thru the whole system.