What is observability engineering?
Observability engineering is the process of designing and building inherently observable systems and leveraging advanced tools and methods to collect, analyze and visualize observability data.
When a system is observable, developers can discern the state of software systems, infrastructure and networking components by analyzing their external outputs. Conventional IT monitoring tools are often incapable of providing complete visibility into today’s intricate software environments, which feature distributed architectures and a litany of microservices and other interdependent components.
Modern software systems and computing environments require modern, full-stack observability tools that provide distributed tracing features and comprehensive metrics and logging functionality. With observability engineering, observability features are baked into development and production systems.
Observability engineers build observability functions into application code, infrastructure and middleware layers and integrate system event data into monitoring pipelines. They use advanced tools that correlate system events across containers, pods, servers and content delivery networks (CDNs) to enable end-to-end traceability in complex cloud-native computing environments.
Observability engineering helps teams analyze monitoring and telemetry data, create more responsive alerting mechanisms, and get more nuanced data visualizations and dashboards. It also supports a shift left observability strategy, which enables developers to proactively detect system issues, understand their root cause and determine the most effective way to resolve them by running observability features earlier in the development lifecycle.
By incorporating observability engineering into their development and network management practices, businesses can create more observable systems that facilitate the delivery of secure, high-availability, high-performing apps and services.
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Observability is the ability to understand the internal state or condition of a complex system based solely on knowledge of its external outputs, specifically its telemetry.
In an observable system, IT teams can more easily monitor and analyze system performance. For example, they can see precisely how data flows across an organization’s tech stack, including applications, on-premises data centers and cloud environments, and where any bottlenecks might be. This insight helps teams identify and remediate issues more quickly, and generally create stronger, more resilient systems.
At its core, observability is about turning raw data into actionable insights. However, unlike traditional monitoring approaches (which focus on predefined metrics and reactive troubleshooting), observability takes a proactive approach.
Observability tools rely on data collection from a broad range of data sources to conduct deeper analyses and accelerate issue resolution. They collect telemetry and other data from various network components (containers, pods and microservices, among others) to provide development teams a holistic view of component health and performance, and that of the larger systems they’re part of.
Telemetry includes the “three pillars” of observability: logs, metrics and traces.
Logs are detailed records of what’s happening within a network and software systems. They provide granular information about what occurred, when it occurred and where in the environment it occurred.
Metrics are numerical assessments of system performance and resource usage. They provide a high-level overview of system health by capturing specific data types and key performance indicators (KPIs), such as latency, packet loss, bandwidth availability and device CPU usage.
Traces are end-to-end records of every user request’s journey through the network. Traces provide insights into the path and behavior of data packets as they traverse multiple devices and complex systems, making them essential for understanding distributed environments.
Unlike monitoring tools, observability platforms use telemetry in a proactive way. DevOps teams and site reliability engineers (SREs) use observability tools to correlate telemetry in real time and get a complete, contextualized view of system health. Thes features enable teams to better understand each element of the system and how different elements relate to each other.
By providing a comprehensive view of an IT environment—complete with dependencies—observability solutions can show teams the “what,” “where” and “why” of any system event, and how the event might affect the performance of the entire environment. They can also automatically discover new sources of telemetry that might emerge in the system (a new application programming interface (API) call to software application, for example).
Telemetry and data correlation features often dictate how software engineers and DevOps teams implement application instrumentation, debugging processes and issue resolution. These tools empower IT teams to detect and address issues before they escalate, helping ensure seamless connectivity, minimal downtime and optimized user experiences.
However, they also provide feedback that developers can incorporate into future observability practices, which makes them integral to observability engineering as well.
Successful observability engineering relies on a few important principles, including:
Comprehensive app instrumentation
Embedding logging, metrics and tracing throughout application codebases helps engineering teams capture critical data at key collection points.
Teams can use structured logging formats (such as JSON) to streamline log management and make logs easier to search and parse. And instrumenting each microservice and third-party integration to collect traces for incoming and outgoing data requests facilitates complete visibility across the IT environment so developers can find and fix issues faster.
Distributed tracing
Distributed tracing tools, which visualize the entire path of each data request in a computing environment, help IT teams quickly troubleshoot problems when they arise.
Developers can use unique identifiers to follow requests as they traverse multiple services, providing complete, end-to-end insight into system operations. For instance, engineers can assign unique trace IDs to every incoming data request at the edge of the ecosystem (at API gateways, for example) and apply span IDs to each segment of the request journey.
Meaningful service level objectives (SLOs)
SLOs are the agreed-upon performance targets for a service over a specific period. They help ensure that businesses can meet service level agreements (SLAs), the contracts between service providers and customers that define the service to be provided, and the level of performance users should expect.
Establishing clear, quantifiable metrics that represent actual user experiences and setting attainable goals for system reliability and performance is integral to observability engineering. This process not only helps ensure that engineers are always working with pertinent observability data, but it also facilitates accurate issue detection and resolution.
Observability-first culture
Observability engineering isn’t just about shifting observability left in the development lifecycle. It’s also about facilitating observability-driven development, where observability practices are integrated into developers’ daily workflows and they drive how engineers create and manage code.
In addition to basic telemetry data and correlation tools, observability engineering relies on:
Establishing robust monitoring protocols is critical to maintaining observable systems. Monitoring tools can continuously collect and track an array of system metrics, including memory usage, error rates, response times and synthetic transaction results. Real-time monitoring helps ensure that engineers have up-to-date information on system behavior.
Most observability solutions also include automated alerting mechanisms that notify teams of anomalous events and deviations from established baselines.
Structured events are data records that contain key-value pairs, which describe a specific activity or occurrence in a system. Transmitting structured events is often the best way to track significant system activities and changes because they capture the context and sequence of operations that led to a particular state or error.
Each event typically includes a unique identifier, metadata (such as headers and variables) and an execution timestamp, making them invaluable for debugging, auditing and forensic analysis.
Application performance monitoring tools provide comprehensive visibility into application health and the end user experience. They can track critical app performance metrics—such as transaction throughput, latency and dependencies between services—that help teams diagnose performance bottlenecks, trace user interactions and understand the impact of changes across the application stack.
Dashboards aggregate and display metrics, logs and traces from different components of the system, offering teams visualized insights that help them quickly assess system performance, identify data trends and pinpoint issues. Dashboards are often customizable, which enables developers to configure them to highlight the most relevant data for each stakeholder’s role in the organization.
Observability engineering is deeply intertwined with DevOps and SRE methodologies.
It provides the data teams need to implement advanced observability practices, such as feature flagging (where new features are turned on or off at runtime to control which users can access them) and blue-green deployments (where developers run two similar, parallel production environments (or clusters) and each environment runs a different release of an application).
By embedding observability engineering into CI/CD pipelines and automation processes, IT teams can enhance overall system reliability, accelerate software delivery and confidently manage changes in the production environment.
Observability engineering encompasses a collection of practices and tools that can deepen visibility into IT environments. It also enables developers to implement more sophisticated engineering techniques, including:
Business KPI correlation
Observability engineering helps teams connect technical indicators (latency, for instance) to key business outcomes (such as customer satisfaction or revenue generation). This approach enables IT personnel to assess the business impact of technical problems, prioritize the fixes that matter most and align technical priorities with organizational objectives.
If, for instance, the observability data shows that higher latency is linked to lower conversion rates, developers can address the latency issues to help increase conversions.
OpenTelemetry (OTel)
OpenTelemetry is an open source observability framework that includes a collection of software development kits (SDKs), vendor-neutral APIs and other tools for application, system and device instrumentation. It simplifies how telemetry data is collected—regardless of programming language, infrastructure or runtime environment—and enables developers to generate, collect and export standardized telemetry data for any observability backend.
With OTel, observability engineers can collect telemetry data consistently across different apps, systems and use cases; streamline data integration and observability practices; and future-proof their IT environments.
Continuous verification
Continuous verification enables developers to embed observability checks directly into the CI/CD pipeline and identify problems before they reach production. Using automated monitoring, logging and alerting features during the build and deployment phases of app development, teams can detect performance issues promptly. These processes help optimize deployment reliability and accelerate the feedback cycle for faster, higher-quality software releases.
Machine learning-driven anomaly detection
Businesses can use AI-powered algorithms to sift through vast amounts of observability data and find emerging system issues that might escape traditional tools. For instance, in a long short-term memory (LSTM) network, machine learning (ML) technology enables the network to better model and learn from data that comes in sequences, such as time-series data and natural language.
LSTMs can be trained on telemetry to identify normal system behavior and predict future system states. If the actual data deviates significantly from the predictions, teams receive an alert notifying them of a potential security breach, network failure or system degradation.
Chaos engineering
Chaos engineering is a process where developers intentionally cause failures in the production or pre-production environment to understand their impact on the system. Simulating disruptions (such as network failures, server crashes or traffic surges) enables observability engineers to identify system vulnerabilities. It also helps them improve their defense posture and incident response strategies and ensure that the system can withstand unexpected event.
- Better anomaly detection and troubleshooting. Observability engineering helps teams quickly spot unusual activity, for faster, more thorough troubleshooting and debugging.
- Faster mean time to repair (MTTR). Observability engineering enables development teams to quickly detect and resolve issues, significantly reducing MTTR.
- Data-driven decision making. The actionable insights observability tools provide can empower teams to make smarter decisions about system architecture, resource management and performance tuning.
- Improved user experiences. Observability engineering helps developers proactively identify opportunities for feature upgrades and optimization so that users have seamless interactions with software and networks.
- Continuous improvement. With observability engineering, DevOps teams get a holistic, in-depth understanding of how their code performs in production, which accelerates bug identification and facilitates continuous improvement.
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Footnotes:
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6 Optimizing LLM Accuracy, Retrieved May 19, 2025, from https://platform.openai.com/docs/guides/optimizing-llm-accuracy.
7 IBM Instana Observability. Retrieved May 19, 2025, from https://www.ibm.com/products/instana.
8 Monitoring AI Agents. IBM Documentation. Retrieved May 19, 2025, from https://www.ibm.com/docs/en/instana-observability/1.0.290?topic=applications-monitoring-ai-agents.
9 Zhou, Y., Yang, Y., & Zhu, Q. (2023). LLMGuard: Preventing Prompt Injection Attacks on LLMs via Runtime Detection. arXiv preprint arXiv:2307.15043. https://arxiv.org/abs/2307.15043.
10 Vesely, K., & Lewis, M. (2024). Real-Time Monitoring and Diagnostics of Machine Learning Pipelines. Journal of Systems and Software, 185, 111136. https://doi.org/10.1016/j.jss.2023.111136