Observability Expanding Beyond Infrastructure and Into AI Systems
Observability revolves essentially around understanding infrastructure health. This means that operations teams monitor applications, netwo0rks, database and cloud environments using familiar signals. They use logs, metrics, latency, uptime measurements, and traces. If systems remain available and the performance stays within expected thresholds, the teams have enough visibility to understand whether applications are functioning properly.
But the arrival of artificial intelligence is reshaping the observability model.
AI systems are increasingly moving beyond experimentation and becoming embedded within operational environments. Organizations are introducing AI at many levels, including automation workflows, support systems, data processing pipelines, agent-based environments, and even decision-making systems. This leads to operational requirements that go beyond traditional monitoring approaches, as teams are no longer observing only infrastructure behavior. Teams are now observing both the system behavior and the system decisions.
Traditional Observability & Deterministic System
Observability has developed around systems that generally behave in predictable ways.
A deterministic system produces expected outputs when working with expected inputs. When an issue arises, the team ops can typically identify the underlying cause through visible symptoms. This is the result of a straightforward investigation.
The type of telemetry signals that are included in traditional observability protocols includes:
- Memory usage
- CPU utilization
- Response times
- Request failure
- Storage consumption
- Network performance
If the application latency suddenly increases, for example, the team ops can investigate the infrastructure utilization, the resource constraints or service failures. Similarly, if a service experiences failures, logs and traces usually provide a relatively clear path to understand the root cause.
This is a realistic and effective approach that relies on the infrastructure behavior following predictable patterns. So, in this type of protocol, observability is designed to identify where something has failed and to determine why it has happened.
AI Systems Introduce New Behaviors
The traditional monitoring approaches work well until you bring AI into the equation. AI introduces new challenges that can’t be monitored using the typical monitoring system.
While deterministic systems are working with expected inputs and outputs, AI systems can produce outputs that vary depending on new elements. These elements can include context, prompts, learning behavior, and even model characteristics. This means that traditional observability will determine that the infrastructure is healthy, even though the quality of results can deteriorate.
This can happen for different reasons, which means that at first glance, a model may operate with healthy latency, healthy infrastructure performance, and acceptable resource usage while at the same time generating inaccurate responses.
This is an important distinction because while traditional observability assumes that health infrastructure means a healthy system, AI introduces challenges that are not monitorable:
- Model drift
- Prompt sensitivity
- Hallucinations
- Changing outputs
- Etc.
In an AI environment, healthy infrastructure could still show degraded outputs. Besides, the same request submitted multiple times could generate different outputs. So, in this context, infrastructure measurements alone can’t explain why results change or why users suddenly encounter low-quality experiences.
Operations teams increasingly require visibility into behaviors that conventional telemetry can’t easily capture.
Observability Is Moving Away From Conventional Telemetry
Traditional observability relies on three core signals, namely logs, metrics, and traces. These signals remain important, even with AI. But using AI systems means it becomes essential to introduce additional forms of telemetry on top. While this doesn’t negate existing forms, there is a need for monitoring AI-relevant signals such as:
- Token consumption
- Interaction cost
- Model confidence
- Response quality
- Prompt quality
- User feedback indicators
- Latency across AI pipelines
These signals are necessary to answer questions that traditional monitoring systems can’t address because they were never designed for it.
Operation teams will be looking at these for questions relating to a sudden cost increase, a change in response quality, or even a workflow suddenly behaving differently.
Ultimately, organizations need to understand not only whether systems are functioning, but whether they are producing useful outcomes. This explains why observability is gradually moving beyond measuring activity only and toward understanding the behavior itself.
Shared Context as An Operational Requirement
Organizations are introducing a large number of AI systems and AI agents. This leads to increasingly complex visibility challenges.
Different systems operate differently. They use different information sources, workflows, and objectives. So, while they may perform effectively on their own, when used simultaneously, they are likely to create fragmented operational environments. This is a big challenge when using AI agents, as this can lead to disconnected information or fragmented decision-making.
AI systems influence actions across multiple environments. So, treating systems as independent components can be a harmful decision that removes contextual awareness from the workflow and the operational processes.
Platforms such as GTM AI introduce shared operational context, which allows systems, AI processes, and workflows to operate with stronger, cross-environment awareness, instead of operating in isolation from each other. For team ops, this is about gaining visibility both into individual systems and the relationships between systems.
Agentic Systems Create New Operational Questions
AI systems have evolved from being passive tools to becoming systems capable of performing actions independently.
These agentic environments can execute workflows and initiate other actions without requiring direct human intervention (automation triggers, etc.) With these capabilities come significant opportunities, but also new operational questions to answer, such as:
- Why did the system take this action?
- Which information influenced the decision?
- What downstream effects occurred?
- Etc.
This highlights even more the need for monitoring environments that are designed around autonomous decision-making processes.
For organizations, observability is a major challenge with agent-based AI systems because team ops need visibility into why actions occurred, rather than just what actions occurred.
AI Observability as a Full-Stack Discipline
As AI environments continue to evolve, observability itself may become a multi-layer discipline.
Future AI observability environments may connect multiple layers for the infrastructure, the model, the workflow, and the business, each with distinctive telemetry signals.
The infrastructure layer, for example, will need to monitor compute resources, storage systems, and networking environments. The model layer should monitor outputs, confidence measurements, and drift behavior. On the other end, the workflow layers can focus on orchestration systems, prompts, and agent interactions. At a business level, we monitor operational costs, user impact, and outcome quality.
Emerging AI observability frameworks increasingly describe visibility as extending from infrastructure through business outcomes, rather than stopping at technical metrics alone.
AI systems are expanding the responsibility toward understanding how a system behaves. As AI environments are becoming more dynamic, the next challenge may be to determine which signals actually matter.