Agentic Observability Is Changing How We Work, and What Matters

If you only listened to the hype cycles, you might think AI has already “solved” observability. But not so fast, my friend.

We can generate code with a prompt. We can summarize incidents in a sentence. We can ask natural language questions of our telemetry. Yet, we still find teams that are drowning in alerts, debating which dashboard to trust, and struggling to understand which issues actually matter most to the business. AI hasn’t made observability obsolete. It’s made it more important—and fundamentally different.

As organizations move deeper into the AI era, most conversations about observability tend to focus on one of two shifts: the rise of AI systems and services that need oversight, or the rise of AI-powered tools that reduce operational toil. Both are real, but neither tells the whole story. AI is reshaping the systems we depend on, how we operate them, and what the business expects in return.

That’s the context for what we call agentic observability, which ensures AI-driven operations are grounded in real system behavior and business impact.

This is our view of where observability needs to go—and how organizations can get ahead of the changes AI is driving.

AI is forcing the next evolution in observability

Every architectural shift has changed what we need from observability. In the early days, monitoring infrastructure was enough because if a server was down, it was a pretty good bet that the application was down. Then came distributed systems and ultimately cloud-native architectures, and hosts failing no longer told you much about a user’s ability to access an application. Observability emerged to help teams make sense of increasingly complex, interconnected environments.

Now AI is pushing us into another transition. Modern applications increasingly depend on models, agents, and orchestration layers that behave in ways traditional signals don’t fully capture. At the same time, engineering and IT teams are turning to AI to automate increasingly complex parts of incident detection, triage, and remediation. The systems themselves are less deterministic. Failures are no longer just outages—they’re silent degradations in quality, cost, or decision-making. And the resulting business impact is often indirect, delayed, or distributed across customer journeys.

Three shifts are happening at once:

• We need to understand the behavior of AI systems themselves—models, prompts, responses, GPUs, and tools—because availability alone no longer tells us whether an AI-powered system is behaving correctly, safely, or cost-effectively.
• We need AI to help us operate at a pace and scale that extends what humans can accomplish, because modern environments generate more signals, interactions, and decision points than manual workflows can reasonably synthesize in real time.
• We need observability to reflect business impact, not just system health, because not every technical issue affects customers, revenue, or risk in the same way.

What agentic observability means

Agentic observability extends the principles of observability into the AI era. It combines AI-driven operational assistance, deeper visibility into AI-powered applications and agents, and a clearer connection between telemetry and business outcomes.

In practical terms, agentic observability uses AI agents and a unified data foundation to fix and prevent issues, observe and govern AI systems, and help teams focus on the problems that matter most to the business. There are three parts to this approach.

1. Fix and prevent with AI agents

For most organizations today, the practice of observability requires an inordinate amount of manual effort. Humans set up instrumentation. Humans tune alerts. Humans comb through dashboards and logs to diagnose issues, often with incomplete context. That’s difficult enough in traditional environments; it doesn’t scale at all when your software relies on models that can learn, drift, and update.

Agentic observability has the potential to shift the balance of work, letting teams stay in control while AI takes on tasks that are repetitive or time-consuming – or, in some cases, beyond the ability of skilled personnel to do, quickly enough. This includes:

• Establishing the right posture by recommending instrumentation, baselines, and SLOs based on the reality of your environment.

• Catching issues earlier by correlating signals across domains and spotting emerging anomalies before they cascade into user-impacting incidents

• Assembling the right context by stitching together traces, logs, network paths, deploys, and business KPIs so engineers aren’t starting from a blank page.

• Taking first action where appropriate by proposing or performing steps like rollbacks, feature flag changes, or capacity adjustments—always with transparency and the ability to override.

The goal isn’t to sideline engineers—it’s to reduce the manual burden so humans can focus on designing systems, improving experiences, and solving the problems that require creativity and judgment.

2. Observe AI agents and the AI stack

As AI becomes embedded in critical experiences, the nature of application risk changes. A model can be “up” while drifting in quality. An agent can complete a task while making poor decisions. Costs can spike with little correlation to traffic.

That’s because both the infrastructure and the applications running on it have evolved.

AI infrastructure and services introduce new components like GPUs, large language models, vector databases, orchestration layers, and agent frameworks that must be monitored. They can also put new demand on related infrastructure, especially networking, memory, and storage. And the monitoring isn’t simply about performance or health, but also about output quality, cost visibility and the efficiency of getting an AI-driven result.

AI systems are also inherently non-deterministic. The same prompt may produce different results over time or degrade subtly without triggering obvious errors. As a result, observability needs to extend beyond binary “working or broken” signals to include quality, safety, and cost—more subjective measures that indicate whether a model or agent is delivering the right outcomes at the right price. To operate AI responsibly, observability has to widen its scope well beyond apps and infrastructure to cover the entire AI stack including GPUs, model endpoints, vector stores, orchestration layers, and the agent frameworks coordinating work. It also needs to capture conversation- and agent-level telemetry, including prompts and responses, tool calls, context windows, and how behavior varies by user or segment.

On top of that, teams must monitor quality and safety signals such as drift, hallucination risk, policy violations, data leakage, and prompt-injection attempts, while tracking cost and efficiency metrics like token consumption, model performance, and the ROI of AI-driven interactions.

This isn’t optional. If AI is driving customer interactions, influencing decisions, or acting autonomously, it needs to be observed with the same rigor as application and infrastructure layers—with added emphasis on quality, trust, and cost over time.

3. Connect signals to business impact

Most environments are a mix of cloud-native services, legacy apps, SaaS components, third-party APIs, and networks you don’t control. Alerts fire without context. Dashboards show symptoms, not impact. And teams waste time chasing issues that don’t actually matter to customers.

Agentic observability starts with the opposite question, “When something changes, what does it mean for the business?” Answering this requires:

• Visibility into end-to-end journeys: checkout, claims, loan approval, order fulfillment—modeled as first-class objects.

• User and segment awareness: which customers, channels, or geos are affected, and how severely.

• Cross-domain context: seeing how infrastructure, services, and networks (owned and unowned) combine to shape an experience.

• Cost and value awareness: prioritizing work based on the outcomes the organization is actually measured on.

This is where a unified data fabric that brings together application telemetry, network insights, security signals, and business context is critical. AI agents need this foundation to deliver real insight—not just faster guesswork.

Why agentic observability matters now

In our State of Observability research report, the organizations seeing the strongest ROI share three traits: they focus on prevention, they resolve issues faster, and they spend significantly more time on innovation instead of firefighting. They treat observability as a strategic capability, not just a toolset.

Agentic observability builds on those foundational practices to prepare teams for the next wave of change that will include a world where more software is generated by AI, more systems behave autonomously, and the growing pressure to connect day-to-day operations directly to revenue impact, risk exposure, and the customer experience.

This is the next phase in observability’s evolution: moving beyond reactive monitoring and system understanding toward confidently operating and governing AI-powered businesses.

And, you don’t need a massive transformation to move in this direction. A few practical entry points:

• Model one critical user journey and tie your existing telemetry to it.

• Introduce AI assistance into your current investigation workflows.

• Instrument an AI-powered application end-to-end and treat it as a pattern to replicate.

• Align engineering and the business on which outcomes matter most before the next incident.

From there, you can expand coverage, add agents, and unify more domains as you go.

AI isn’t making observability disappear. It’s raising expectations. Agentic observability is about meeting those expectations and using AI to operate smarter, observing AI systems responsibly, and grounding every decision in real business impact.

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