Observability vs. monitoring: Why you need both for your intelligent automation systems
Is your automation system really under control—or are you only scratching the surface of what’s happening behind the scenes?
Monitoring and observability – two concepts in the world of IT operations and automation that play a crucial role in ensuring the health, reliability, and efficiency of systems. While they may seem similar, they serve distinct purposes.
You can imagine monitoring like a security camera that is keeping an eye on key metrics and alerting when something goes wrong. Observability, on the other hand, is more like a proactive investigator—analyzing logs, traces, and other data to uncover the root cause of an issue.
For companies that highly rely on automation, understanding these differences is essential to keeping systems running smoothly and preventing costly downtime. Let’s explore how both work and why they are best used together.
Monitoring as the first line of defense
Monitoring is the foundation of system reliability. It focuses on collecting predefined metrics and logs to track the health and performance of systems. It provides metrics such as:
- Process execution times
- Error rates in automation workflows
- Failed RPA bot runs
- CPU and memory usage trends
How monitoring works:
- Monitoring tools such as Pointee continuously collect data from automation platforms and infrastructure.
- Alerts are triggered when predefined thresholds are breached (e.g., a spike in bot failures or response time slowdowns).
- Automation teams receive these alerts and take corrective actions.
Limitations of monitoring
While monitoring is excellent at detecting known issues and setting up alert-based responses, it has limitations:
- It only tracks predefined metrics, meaning unknown problems may go undetected.
- Alerts often lack context, making it harder to diagnose the root cause.
- It’s reactive rather than proactive—it tells you when something is wrong but not always why.
Observability gives you the bigger picture
Observability goes beyond monitoring by providing deep insights into system behavior. It involves collecting, analyzing, and correlating different types of data to understand the why behind issues.
Key components of observability:
- Metrics: Time-series data reflecting system performance.
- Logs: Records of events, errors, and transactions.
- Traces: End-to-end tracking of automation workflows and service interactions.
How observability works
Instead of just raising an alert when something goes wrong, observability helps automation teams explore logs and traces to understand the root cause. Machine learning and Pointee’s Predictive Analytics can detect anomalies before they escalate. Engineers gain deeper insights into system performance which will allow them to optimize automation workflows further.
Why observability matters for automation
Automation processes often involve multiple interconnected systems, making it difficult to pinpoint failures. Observability enables teams to:
- Identify complex failure patterns (e.g., why a bot failed despite no errors in the script).
- Correlate failures with recent changes (e.g., did a software update impact automation performance?).
- Reduce resolution times by providing full visibility into automation logs and execution paths.
Conclusion: How to get the best of both worlds
By combining monitoring and observability with Pointee’s AI-driven platform, automation teams can proactively prevent failures, optimize workflows, and achieve greater system resilience.
Pointee’s AI-powered intelligent automation platform brings the best of both worlds to help teams stay in control of their automation environments.
