Is AI bringing application observability and behavior tracking together?

For decades, product teams and engineers have lived in parallel worlds.

One watched servers. The other watched users. But AI might finally be forcing them to share a dashboard.

Two worlds, two audiences

On one side sat the application and infrastructure observability stack—the domain of developers and SREs. Their toolkit: Datadog, Prometheus, Grafana, and more recently, OpenTelemetry. Their goal: ensure the system is healthy, performant, and resilient. They measure traces, metrics, and logs, which are the vital signs of code and infrastructure.

On the other side lived the user behavior tracking stack, owned by product managers, marketers, and growth teams. They relied on RudderStack, Segment, Snowplow, and Amplitude to understand the human side of applications: where users came from, where they dropped off, and what drove engagement or churn. Their data powered dashboards, cohorts, and campaigns.

These two ecosystems rarely meet. One explained what the system did; the other explained why the user did it. Looking at a chain of function calls could not easily tell you that a user abandoned their cart, and a funnel chart could never tell you which API call slowed them down.

Enter agentic applications

AI-powered “agentic” applications, from customer support copilots to workflow agents, are breaking down this wall.

In these systems, intent and execution live side-by-side. When a user says, “Recommend a white running shoe,” that text becomes the ground truth of intent. The application’s job is to interpret and fulfill it, often through a chain of context engineering, LLM calls, tool executions, and retrievals.

Debugging such an app no longer means checking for crashes or latency. It means asking, “Did the agent correctly understand what the user wanted, and did it take the right steps to deliver it?”

That’s no longer pure infrastructure monitoring. It’s intent observability, something closer to a product problem than a DevOps one.

Intent observability also includes monitoring the system for cost, model performance and reliability, model A/B testing and so on.

When logs meet journeys

Here’s the flip side: In these systems, there’s no separate behavioral tracking layer. The user’s natural-language input is the behavioral signal. There’s no need for clickstream instrumentation or synthetic events — the conversation itself captures everything.

From the same unified trace, you can:

• Trace user journeys (intents, satisfaction, outcomes).
• Execute user segmentation (users who have expressed specific intents)
• Power activation loops (“Users whose requests failed → follow-up campaign”).
• User-level billing (charge users for model costs)

Observability and analytics begin to merge. System logs and user events collapse into a single semantic layer.

A unified future?

This leads to an intriguing possibility: Will AI give rise to a unified observability-behavior stack, where traces, intents, and outcomes coexist?

Will new entrants build from scratch? Or will today’s giants evolve? Maybe Datadog adds “intent traces.”Maybe Amplitude adds “execution traces.” Maybe the next platform does both from day one.

The early signals are already here: OpenTelemetry is expanding beyond infrastructure metrics into application-level business events. Analytics tools are experimenting with LLM-based session understanding. AI observability startups are blending embeddings, traces, and feedback loops.

In AI-native products, understanding user intent and system behavior are no longer separate disciplines. They’re two views of the same log.

The takeaway

AI is going beyond simply reshaping applications. It’s reshaping how we see them. The next generation of observability tools won’t just ask, “Why did the app crash?” They’ll ask, “Why did the agent fail to understand the user?”

And when that happens, product managers, data engineers, and SREs may finally be looking at the same screen.

The bottom line? The future of observability is conversational.