Simplify the operational complexity of cloud-native services with smart observability

[HPE_Experts]

Cloud-native platforms enable microservices, AI, and edge, but neglected ops cause issues. Agentic AI observability simplifies complexity and boosts reliability.

Agentic AI-powered smart observability for cloud-native services: Empowering microservice, AI, and edge workloads.

Cloud-native architecture has become the backbone for enterprises running microservices, AI, and edge workloads. It offers strong business values such as agility, scalability, and resilience. However, in the real world, operational aspects are often overlooked during development and migration, leading to significant challenges during service launch and ongoing operations. To overcome these challenges, enterprises must move beyond traditional monitoring and embrace next-generation observability—now being transformed by agentic AI.

This article explores how smart observability, empowered by agentic AI, addresses the operational complexity of cloud-native services—especially microservices, AI, and edge workloads—ensuring autonomous and resilient cloud-native operations.

1. Business challenges caused by neglecting operational aspects

The adoption of cloud-native technologies such as containers, Kubernetes, serverless, and microservices leads to an explosion in the number of monitoring targets and metrics. Furthermore, problem-solving is no longer a simple matter of Is the server down? but has become complex and multidimensional, such as Why is the AI model latency increasing in one edge zone? or, Which microservice in a complex dependency chain is throttling throughput?

Let’s examine the major business challenges faced by cloud-native applications of microservices, AI, and edge workloads that neglect operational considerations.

1) Decline in service availability and business reliability

Developing applications into independent service units, known as microservices, necessitates dealing with issues that did not occur in traditional monolithic development. A single service failure can cascade across dependencies—especially when running AI or real-time edge applications—causing full-service degradation. A common example is when a long service chain causes even a small delay to lead to a total transaction timeout, degrading user experience, and resulting in failed transactions. Gartner defines this issue as new failure modes and states: Microservices architecture introduces new failure modes that can significantly increase MTTR and reduce service availability if not properly managed. Furthermore, it analyses that as service dependencies grow, the risk of cascading failures increases, potentially raising the overall transaction failure rate by 30–40%.¹ When such failures occur, the operation team must manually sift through countless logs and trace information to determine the root cause. This lengthens the mean time to resolution (MTTR), undermining service availability, ultimately making the service unstable, reducing availability, and causing the business to lose reliability.

2) High operational costs and inefficiency

Cloud-native architectures have presented operations team with unprecedented complexity. Dozens or hundreds of microservice instances run independently, constantly generating logs, metrics, and events. Moreover, it is nearly impossible to manually track issues arising from the complex call relationships (dependencies) between distributed services. Furthermore, the operation team is often required to possess expertise across all of the development team’s technology stacks, which leads to an increase in management costs and operational overhead. Without resource optimization and cost management during development, excessive resource usage in the production environment leads to unnecessary costs. Situations have emerged where not only preventing excessive resource use due to over-provisioning, but also identifying and reallocating unused resources must be supported. Gartner has analysed that requiring operations teams to understand the entire technology stack used by development teams is a major factor contributing to an average increase of 25–35% in management costs. Additionally, it notes that in cloud-native environments, failing to identify application hot spots can lead to excessive resource usage, potentially resulting in cost overruns of up to 40%.²

3) Slowed innovation

Cloud-native systems are independently developed and deployed based on microservices with very frequent deployments for updates and new feature releases. Frequent deployments are essential for innovation, especially in AI-driven services. When frequent deployments are combined with manual deployment, configuration omissions and inconsistencies inevitably occur, resulting in phenomena like It worked fine in the test environment.

Unstable deployments make teams hesitant to deploy new features, and because it’s difficult to predict the impact of new code or services on the current system, deployment cycles become longer or rollbacks become frequent, slowing the pace of service innovation.

2. Solution to cloud-native complexity: Smart observability is essential.

Traditional monitoring based on performance metrics and failure events was effective for operating monolithic applications built on a traditional three‑tier architecture. However, such monitoring approach is insufficient to address cloud‑native complexity. Some failures cannot be resolved by simply rebooting servers. Instead, they require complex problem-solving capabilities such as analyzing the root cause of overload and the interdependencies between services. This shift demands a transition from traditional monitoring to end-to-end full-stack observability—integrating metrics, traces, and logs to comprehensively understand system behavior. True observability smartly connects these three core data types organically to provide a smart, holistic view of system operations.

• Microservices: Smart observability reveals inter-service dependencies, enabling root cause analysis of cascading failures
• AI workloads: Smart observability tracks model performance, latency, and resource usage across distributed pipelines
• Edge workloads: Smart observability ensures visibility across geographically distributed nodes, detecting anomalies in real time

In today’s cloud-native environments, where microservices and container-based infrastructures—especially today’s distributed AI and edge workloads—are prevalent, end-to-end full-stack observability is no longer optional—it’s essential.

3. Agentic AI for next‑gen smart observability: Beyond AIOps to autonomous operations

When a failure occurs, the operation team resolves it through the stages of failure detection, failure analysis, and failure response. AIOps assists the operations team in problem-solving by learning from past data and current patterns at each of these stages.

But AIOps is now evolving. The trend is moving toward integrating Agentic AI—which understands goals, implements autonomously and learns from outcomes—directly into the observability stack.

Let’s explore how agentic AI enhances observability.

1) Autonomous incident detection

• Learns baselines for microservices, AI inference workloads, and edge node behavior
• Detects anomalies early—before users experience issues
• Identifies unusual model latency, GPU saturation, or edge zone degradation

2) Autonomous root cause analysis

• Correlates logs, traces, metrics, and events
• Analyzes complex microservice dependency chains
• Determines if an AI model bottleneck is causing downstream failures
• Identifies edge issues caused by network instability or hardware variance

3) Automated remediation and optimization

• Automatically scales microservices or AI model-serving instances
• Reallocates compute resources based on real-time usage
• Fixes common issues with infrastructure as a code (IaC) (Terraform, Helm, Ansible)

4) Less human effort operations

• Automate repetitive tasks such as daily health checks and weekly reporting
• Summarize service states and recommend optimizations, freeing operators to focus on innovation

4. Approach to build agentic AI-powered smart observability

To implement agentic AI-powered smart observability for microservices, AI, and edge workloads:

• Set clear goals, monitor, and evaluate: Define measurable objectives such as latency reduction, cost optimization, or AI model uptime. Continuously assess performance and adjust as needed.
• Strengthen and integrate operational data quality: Unify and standardize logs, metrics, and traces across microservices and edge nodes to ensure high-quality data for AI training and analysis.
• Secure sensitive data and ensure compliance: Protect credentials and sensitive data, especially across distributed AI pipelines, and adhere to regulatory requirements.
• Integration with open ecosystems: Leverage open frameworks such as Prometheus, Loki, OpenTelemetry, Robusta, and Keptn for seamless observability and automation.

5. Business benefits

Companies can obtain the following business benefits through cloud-native service operations utilizing agentic AI-powered smart observability:

• Improved customer service reliability: Proactive and predictive operations minimize service interruptions, improving customer service reliability
• Reduced operational costs: Automated problem-solving and reduced manual work decrease labor and infrastructure costs
• Faster innovation cycles: Autonomous stability enables confident, frequent deployments of AI models and new microservices
• Ease-of-service expansion: Ensures stable service expansion when extending to multicloud and hybrid environments

Conclusion

The shift to microservices, AI, and edge workloads is inevitable—but so are the operational challenges. Without agentic AI-powered smart observability, your services may become unmanageable. Manual processes can’t keep pace with the speed and scale of modern workloads.

Agentic AI-based smart observability isn’t just an upgrade—it’s a necessity for sustaining cloud-native agility and reliability:

Smart observability provides visibility. Agentic AI provides the autonomy. Together, they deliver operational excellence.

HPE’s deep expertise in cloud-native transformation, combined with solutions such as HPE Morpheus Enterprise Software and HPE OpsRamp Software, enables enterprises to achieve proactive, resilient, and autonomous operations powered by agentic AI.

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¹ “How to Succeed With Microservices Architecture for Cloud-Native Applications,” Gartner, November 2024.

² “Use Cloud-Native Architecture to Modernize Your Applications,” Gartner, July 2024.

Meet the author:

Jikyun Kim, Principal Information Systems Architect