Agents Overview

This dashboard provides a centralized view of agent activity across all distributed agent servers in a Relativity environment. It provides key metrics on agent performance, load distribution, and job throughput, helping relativity administrators monitor agent health, validate deployments, and identify trends that may impact system performance. This dashboard supports routine operational monitoring, post-installation validation, and troubleshooting scenarios where agent behavior or job processing efficiency is a factor.

Agent Performance Summary

The metric indicators at the top of the dashboard highlight agent performance across the environment. These include overall agent success rate, number of running agent job types, total agent jobs processed, and average job duration. These indicators help relativity administrators quickly evaluate whether agents are executing reliably, whether job volume is within expected ranges, and whether processing times suggest healthy system performance. The distribution of jobs by host provides insight into how workload is spread across agent servers. The chart helps identify whether processing is balanced or if certain hosts are carrying a disproportionate share of the execution load. Imbalances can indicate configuration issues, server resource constraints, or uneven agent deployment. Agent type distribution by host table lists each agent type along with the number of instances running on each host. This enables administrators to verify that agents are deployed as expected across the environment. The distribution helps detect missing agents, incorrect host assignments, and deployment inconsistencies that may impact workflows dependent on specific agent types. Totals at the bottom summarize deployment density across hosts for easier comparison.

Agent Job Volume and Duration

This section lists the most active agent types in the environment, ranked by job count, along with their average execution duration. It helps administrators identify which agents are processing the highest volume of work and assess whether job durations align with expected performance benchmarks. High job counts combined with unusually long durations may indicate resource constraints or configuration issues requiring attention. Conversely, consistently low durations for high-volume agents suggest efficient processing and healthy system performance.

Use Cases

Use Case	Description
Validate agent deployment	Confirm that each agent type is deployed to the appropriate servers after installation, scaling, or configuration updates.
Monitor agent performance	Track success rates, job duration, and job volume to ensure agents are processing tasks efficiently.
Detect workload imbalance	Use host-level job distribution to identify uneven processing loads and target optimization efforts.
Troubleshoot agent-related issues	Leverage agent distribution and performance metrics to isolate problematic hosts, agent types, or job behaviors.