Observability trends 2026

AI-driven observability tools can automate decision-making based on the telemetry data they gather, integrate data visualization into dashboards via generative AI and optimize workflows with the insight gained through machine learning. The new layer of complexity that AI introduces will require vigilance when it comes to monitoring costs, breaking down silos and ensuring compatibility and functionality across a full stack of distributed systems.

Therefore, three crucial trends in the 2026 observability landscape will be:

1. Observability platforms becoming more intelligent to keep up with AI
   
2. Using observability as part of an overall cost management strategy
3. The increased adoption of open observability standards

Making observability platforms more intelligent will be vital as more systems come to integrate and depend on AI-powered IT. Observability intelligence requires the increased use of AI-driven observability tools—essentially, using AI to observe AI.

When it comes to managing costs, effectively deploying observability tools in a cloud-native environment requires special attention to pricing and compatibility. Improved forecasting and capacity planning and a focus on service level objectives can help keep spending in line and avoid vendor lock-in.

Observability standardization is necessary as open-source telemetry standards and tools—such as OpenTelemetry (OTel), Prometheus and Grafana—adapt to the use of generative AI in their workloads. The use of a common standard can allow organizations to integrate the observability data produced by generative AI tools, machine learning models and AI agents with the rest of their stack, providing a more comprehensive view of system performance and metrics.

Other key trends in observability include observability as code, a DevOps practice in which observability configurations are managed like code, and an increased focus on observability for business-critical functions as organizations seek to better manage a growing number of observability alerts.

AI tools require new practices for gathering and using data. Many organizations will need to overhaul their current observability practices to make sure AI tools are understood, efficiently deployed and aligned squarely with business goals.

In observability terms, “intelligence” is the basic gathering of telemetry data from IT systems, as well as the ability to use that data to detect anomalies, perform root cause analysis, troubleshoot issues, improve user experience and ultimately forecast problems to prevent them from happening.

“In 2026 more aspects of the world will be handled by AI systems, which ultimately run on infrastructure that can fail in various ways,” Arthur de Magalhaes, a senior technical staff member for AIOps and the Instana observability platform at IBM, told IBM Think.

“The intelligence and speed required to keep these AI systems healthy also grows in parallel, demanding that more innovative and powerful types of intelligence are implemented.”

de Magalhaes told IBM Think that the biggest trend in 2026 for observability intelligence is the increased integration of agentic AI, with AI agents ingesting the necessary observability data and insights to accomplish their goals. For example, an agent that specializes in handling logs can analyze those logs, extract patterns, find anomalies and then work with other agents that have different capabilities to remediate and prevent disruptions, potentially improving mean time to repair (MTTR).

Agents are also capable of scaling resources, rerouting traffic, restarting services, rolling back deployments and pausing data pipelines, among other tasks. Increasingly, they do this while acting on parameters set by automated decision engines which decide whether an issue requires action, what kind of action is appropriate and how urgent it is based on business needs.

Delegating these governance decisions to an agent requires observability data to back up those decisions. An observability solution that effectively integrates AI agents can observe the results of actions, adjust models and policies and improve future decisions with minimal human intervention.

According to research published in January 2026 by Omdia,¹ 55 percent of business leaders polled said that they lack the necessary information to make effective decisions about spending on technology. The growth of AI can further complicate the matter.

“Companies that provide a service which exposes AI features need to proactively observe their internal GPU cost and dynamically scale up and down to meet demand while remaining profitable,” said de Magalhaes. Observability practices are crucial for striking that balance.

Observability can help organizations evaluate network performance and determine when and where IT investments should be made.

As expensive AI tools like agents and large language models (LLMs) drive demand for expensive graphics processing units (GPUs), it will be paramount for organizations to place and use these GPUs efficiently, so customers retain access to AI tools with minimal outages. Observability data can help with optimizing the placement and usage of these GPUs so that users can access AI tools without the organization running into negative profits or passing cost onto the user.

Agentic AI has a role to play in managing these costs. In one use case, agents specialized for AI observability might be able to analyze data from hybrid, multicloud environments to optimize GPU purchasing and placement, resulting in real cost reductions

Observability can help manage costs in other aspects of corporate IT, too. For example, consider the use of observability tools to compare different IT ecosystem configurations and network topologies, with the goal of reducing observability costs while maintaining (or improving) performance targets for observability tools.

Capacity planning, or the process that examines an organization’s production capacity and the resources needed to meet goals, can also play a role in controlling costs, powered by real-time insights gained from observability tools and monitoring tools.

With more generative AI models in the tech stack, a common standard will be required to integrate them with existing observability tools and data sources.

Standardization in observability refers to the adoption of common specifications and frameworks for observability data, often at the instrumentation level where code is used to collect telemetry.

Common standards can streamline data ingestion, foster innovation in the field and help to avoid vendor lock-in—which will be crucial as generative AI tools, often owned by third-party providers with limited visibility into their inner workings, become more integrated into cloud-native IT environments.

“Community and enterprise adoption are the most important factors for standardization in observability,” de Magalhaes told IBM Think. “Standards need to be accepted and adopted by large community groups, and shortly after there must be proper support from enterprise vendors to ensure these standards can be applied to real-world scenarios.”

According to de Magalhaes, OpenTelemetry will continue to grow its generative AI observability capabilities in 2026. OTel’s common data standards could allow observability vendors to correlate telemetry from black-box gen AI tools with the rest of the IT environment, creating a more thorough, end-to-end view.  

Other key trends for 2026 include the growth of observability as code and an increased focus on observability for business-critical functions.

The growing adoption of open standards is tied to the adoption of observability as code. According to Splunk’s “State of Observability 2025” report, 57 percent of frequent OpenTelemetry users now report that they “often” or “always” deploy observability as code (OaC).

Observability as code is a DevOps practice that applies the principles of software development to observability. Similar to infrastructure as code (IaC), observability as code involves managing observability systems and policies through the creation of configuration files, which are version-controlled and managed through pull requests. These files replace the manual navigation of observability tools and user interfaces with a process that mirrors the deployment of code.

“The same tools and concepts that govern and execute infrastructure as code also apply to observability as code,” said de Magalhaes.

Observability as code means the same CI/CD pipelines that automatically track and deploy software code can also be used to govern observability, enabling the automatic gathering, analysis and retention of telemetry data. An environment governed by open standards makes the deployment and editing of this code more seamless across diverse network environments.

The configuration files created in an OaC environment define how telemetry is collected, visualized and evaluated—such as through instrumentation rules, alerts, dashboards and SLOs. Administrators can ensure that when IaC tools spin up a new server to meet demand, for example, an accompanying configuration is generated for gaining observability of that server.

As observability tools become more powerful and widely used, organizations will need to focus their observability efforts on systems that directly impact business outcomes.

Gaining better observability of systems over time creates an accompanying risk of greater alert fatigue. According to research published in November 2025 by Omdia², alert fatigue is by far the greatest concern for cybersecurity teams in the sensitive field of operational technology, highlighting how important it is for IT teams to intelligently and swiftly sort alerts and discard those that are irrelevant or redundant.

According to de Magalhaes, the most frequently requested way to reduce alert bottlenecks is to limit alerts to those that impact business outcomes. Therefore, organizations might consider developing specific observability strategies for the parts of the network that directly execute business operations.

For example, site reliability engineers (SREs) might develop a rule to distinguish in their anomaly detection between a host server running out of memory in a test environment—an issue with relatively low urgency—and a host running out of memory in a production environment that approves credit card transactions, something that should immediately spark an incident response.