Instead, they say:

• “Salesforce is freezing.”
• “Teams calls keep breaking up.”
• “The application is painfully slow.”

As a result, IT Operations teams must quickly determine not only that users are struggling, but also why.

Modern Digital Employee Experience (DEX) platforms do an excellent job identifying user impact. They surface slowdowns, crashes, login failures, poor SaaS performance, and widespread degradation across departments or regions. However, detecting frustration is only the first step.

IT teams still need to answer critical operational questions:

• What changed?
• Which dependency failed?
• Did the issue originate in the application, infrastructure, or network?

This is exactly where the integration between Â鶹ѧÉúľ«Ć·°ć Aternity employee experience and Â鶹ѧÉúľ«Ć·°ć APM+ changes the troubleshooting workflow.

The Gap Between User Experience and Application Root Cause

Today, most IT organizations still troubleshoot using disconnected tools.

Typically, DEX platforms identify that users are affected, while application observability tools monitor transactions and services separately. Meanwhile, infrastructure and network teams investigate performance issues in parallel.

Consequently, IT teams often spend valuable time manually correlating data, switching between consoles, escalating issues across teams, and rebuilding timelines by hand.

In other words, DEX tools often answer: “Users are affected.”

But IT still needs another workflow to answer: “What actually happened?”

Start with the User Impact

Aternity gives IT Operations a far better starting point because it immediately shows where digital experience is degrading and who is affected.

For example, IT teams can quickly identify slow application response times, login degradation, crashes, freezes, and the geographic or departmental scope of the issue. At the same time, Aternity correlates device and network conditions that may contribute to poor experience.

More importantly, Aternity connects technical issues directly to business impact. Teams can immediately understand which users and applications are affected and how productivity is suffering.

As a result, IT shifts from infrastructure-first monitoring to experience-first operations.

Instead of starting with an infrastructure alert and searching for impact afterward, teams start with the actual employee experience and work directly toward root cause.

Move Directly from Experience to Application Root Cause

Once Aternity identifies an issue, IT teams can launch directly into APM+ with the correct application and time context already preserved.

That transition dramatically streamlines the investigation process.

Rather than forcing teams to manually reconstruct events across disconnected systems, Â鶹ѧÉúľ«Ć·°ć creates one continuous workflow from user impact to application root cause.

From there, APM+ reveals what happened inside the application environment, including transaction traces, service dependencies, slow database calls, API failures, and latency across microservices.

As a result, teams can move beyond symptoms and isolate the underlying cause much faster.

Simply put, Aternity answers: “Who felt the issue?”

While APM+ answers: “What caused it?”

Together, they connect user experience directly to application behavior.

Accelerate Resolution with Shared Context

When DEX and application observability work together, IT teams spend less time chasing symptoms and more time resolving problems.

Instead of manually correlating siloed data, teams can move seamlessly from user complaint to impacted application to transaction-level root cause.

Consequently, teams reduce operational friction while accelerating resolution.

In addition, APM+ delivers full-fidelity visibility designed for IT Operations. Therefore, teams can investigate issues with complete context instead of relying on sampled data or partial traces.

Ultimately, the integration helps organizations:

• Accelerate root cause identification
• Reduce escalations and manual troubleshooting
• Lower MTTR
• Align user experience with operational insight

Digital experience problems rarely originate in a single domain. While employees experience the symptom first, the root cause often lives deep inside modern application architectures.

By connecting Aternity with Â鶹ѧÉúľ«Ć·°ć APM+, IT Operations teams can finally bridge that gap, moving from user impact to application root cause through one continuous workflow.

Explore the Aternity to APM+ workflow, request a demo.

But instrumentation is only the first step.

As organizations begin introducing OpenTelemetry into their environments, often alongside existing observability tools, incident response remains a challenge. Even with additional telemetry, operations teams frequently lack access to the context required to diagnose issues quickly, explain user impact, and restore service with confidence.

This is where Â鶹ѧÉúľ«Ć·°ć APM+ comes in.

APM+: Complete Transaction and Code‑Level Views for Operations

APM+ builds on OpenTelemetry to deliver application observability that works for IT Ops, not just developers.

Instead of sampling, APM+ retains full‑fidelity, end‑to‑end transaction context at up to 1 second granularity. Every transaction is captured independently, making anomalies, outliers, and early degradation visible the moment they appear.

That transaction context extends directly into application code. Method‑ and class‑level execution data shows exactly where time is spent, where errors occur, and how execution paths behave under real production conditions. For Ops teams, this means fewer hand‑offs, less guesswork, and faster root cause analysis.

Instrument Everything Without the Cost

Instrumentation costs often force IT Operations to limit observability. APM+ and OpenTelemetry makes broad instrumentation possible.

As application coverage grows, ingestion‑based pricing forces teams to limit observability to Tier‑1 applications, re‑introducing blind spots across the broader portfolio. IT Operations is left with partial visibility precisely when broad coverage matters most.

Â鶹ѧÉúľ«Ć·°ć APM+ changes the economics of application observability. Priced at up to 70% less than traditional APM tools, it removes the cost barriers that typically restrict deep visibility to a small set of Tier‑1 applications.

But lower pricing alone isn’t enough to make broad OTel instrumentation sustainable. As coverage expands, controlling telemetry growth becomes just as critical as reducing data storage costs. That’s where Â鶹ѧÉúľ«Ć·°ć Smart OTel comes in.

Smart OTel applies intelligence at the point of collection, filtering low‑value noise before it drives cost, while preserving high‑value transactions at full fidelity. The result is predictable cost and sustainable coverage, even as application footprints grow.

For IT Ops, that means broader application visibility without sacrificing depth…or the budget.

From Visibility to Faster Operational Resolution

For operations teams, seeing an issue isn’t enough, resolution speed is what counts.

APM+ correlates transaction data with dependencies, infrastructure behavior, and user experience. When performance degrades, IT Ops can move directly from detection to diagnosis with the right context already captured.

There’s no need to reconstruct timelines across disconnected tools. The execution details required to understand impact and cause are already there—accelerating MTTR and improving confidence during incidents.

OpenTelemetry Is the Foundation. APM+ Makes It Operational.

OpenTelemetry enables organizations to instrument everything. APM+ permits IT Ops teams to detect, diagnose, and remediate everything.

Together, they eliminate the trade‑offs that have historically limited application observability in operations. Teams no longer have to choose between broad coverage and deep insight, or between controlling cost and maintaining full‑fidelity, actionable visibility.

Click here to learn more about Â鶹ѧÉúľ«Ć·°ć APM+ or request a demo.

The constraint isn’t desire or awareness. It’s the economic and operational reality of applying deep observability across modern application environments at scale. That’s why OpenTelemetry (OTel) is emerging as a critical foundation for modern application observability.

The Observability Gap Most Enterprises Live With

As application environments scale, traditional APM approaches become economically impractical to apply broadly, forcing teams into difficult tradeoffs:

• Focus deep instrumentation on tier‑one applications, leaving the long tail largely invisible
• Or absorb pricing tied to telemetry volume to deploy more broadly

The result is a fragile operating model. Teams pay heavily for observability but still face blind spots across much of the application portfolio. When incidents occur outside the handful of deeply instrumented services, troubleshooting becomes slow, manual, and dependent on guesswork.

As environments become more distributed—and as AI workflows introduce new dependencies and execution paths—these blind spots only multiply.

Why OpenTelemetry Changes the Game

OpenTelemetry fundamentally shifts what’s possible. As an open, vendor‑neutral standard, it makes deep, consistent instrumentation feasible across the entire application estate, not just a select few services.

With OpenTelemetry, teams gain:

• Standards‑based instrumentation across languages, frameworks, and platforms
• Freedom from proprietary agents and data formats
• The ability to instrument cloud‑native, hybrid, and legacy environments consistently
• A common telemetry foundation that can evolve as architectures change

Most importantly, OpenTelemetry removes the structural barriers that historically limited observability coverage. It becomes realistic to instrument all applications, not just what the budget allows.

This is a necessary step toward modern application observability, but it’s not sufficient on its own.

The Hidden Challenge of “Instrument Everything”

OpenTelemetry makes it technically possible to capture telemetry everywhere. At enterprise scale, however, raw telemetry volume grows fast—and often uncontrollably.

As coverage expands:

• Trace volume increases exponentially
• Storage and ingestion costs rise
• Signal‑to‑noise ratio drops
• Operations teams struggle to find what actually matters

Streaming all raw telemetry downstream without governance simply shifts the problem. Instead of blind spots, teams face data overload, higher costs, and operational complexity that undermines the very value observability is meant to deliver.

This is where many organizations stall. Open instrumentation without intelligent control becomes unsustainable.

The Missing Layer

OpenTelemetry makes it possible to instrument everything, but at enterprise scale, raw telemetry quickly becomes overwhelming. As teams expand instrumentation across applications, services, and AI workflows, telemetry volume grows exponentially, driving higher storage costs, increased noise, and operational complexity. Without controls at the point of collection, “open” observability can become unsustainable.

Â鶹ѧÉúľ«Ć·°ć Smart OTel is purpose‑built to solve this problem.

Smart OTel is Â鶹ѧÉúľ«Ć·°ć’s intelligent telemetry governance technology, designed to turn open instrumentation into scalable, enterprise‑grade observability. Instead of streaming unfiltered telemetry downstream, Smart OTel applies intelligence at the point of collection, filtering, enriching, and shaping telemetry in real time—before data overload becomes an issue.

With Smart OTel, Â鶹ѧÉúľ«Ć·°ć ensures:

• High‑value signals are preserved at full fidelity, including complete transaction context
• Low‑value, redundant, or noisy data is intelligently reduced or filtered
• Trace depth and execution context are maintained, even as coverage expands
• Telemetry volume and cost remain predictable and controlled

This approach allows organizations to confidently scale OpenTelemetry instrumentation across their entire application estate, without runaway observability costs or sacrificing the depth required for fast, accurate troubleshooting.

Crucially, Smart OTel is built on open OpenTelemetry standards. Telemetry remains portable, vendor‑neutral, and aligned with OpenTelemetry as observability strategies, architectures, and tools evolve—while benefiting from Â鶹ѧÉúľ«Ć·°ć’s intelligence and operational expertise.

From Raw Signals to Operational Intelligence

When OpenTelemetry is paired with Smart OTel, observability moves beyond basic monitoring.

Teams gain the ability to:

• Preserve complete transaction context across applications and dependencies
• Detect degradation early through correlated signals and baselines
• Move directly from alert to root cause using captured execution context
• Extend observability into AI agents and workflows, making AI behavior transparent and diagnosable

This intelligent telemetry foundation also enables higher‑order capabilities such as AI‑assisted root cause analysis, guided remediation, and over time, more autonomous operations.

Modern enterprises don’t run tens of applications; they run hundreds. Observability strategies must scale accordingly.

Observability That Finally Scales with the Business

OpenTelemetry provides the openness and flexibility required to instrument everything. Â鶹ѧÉúľ«Ć·°ć Smart OTel ensures that doing so remains economically viable, operationally manageable, and analytically useful.

Together, they eliminate the historic tradeoff between depth and breadth, making it possible to achieve full‑fidelity application observability across the entire application estate, not just the most critical services.

That’s the difference between limited monitoring and real production‑level understanding,  where every transaction, every dependency, and every user impact is visible when it matters.

To learn how Â鶹ѧÉúľ«Ć·°ć APM+ supports OpenTelementy, click here.

It is a reasonable place to start. Accuracy shows whether a model produces correct responses and helps data science teams validate training approaches, compare architectures, and improve results.

But accuracy alone says very little about how AI behaves once it leaves the lab and enters the enterprise.

When AI becomes part of real workflows, supporting employees, serving customers, or informing business decisions, accuracy alone is no longer sufficient.

Accuracy Is Necessary, But It’s Not Operational Intelligence

An AI model can be accurate and still fail the business.

Accuracy does not capture:

• Latency: A correct response that arrives too late degrades user experience just as surely as an incorrect one.
• Cost: Inference pricing, token consumption, and compute usage rarely appear in accuracy metrics, but they surface quickly on a bill.
• Adoption lag: A model can be accurate and still unused. Accuracy does not indicate whether AI is trusted, adopted, or delivering value.
• Dependency impact: Many AI agents sit inside workflows, triggering actions or passing outputs downstream. When something breaks, failures often propagate silently.

In short, accuracy measures what an AI produces. It says very little about how the system behaves.

The Real Challenges of AI in Production

At runtime, AI is subject to the same pressures as any other production technology. Performance fluctuates. Costs vary. Usage ebbs and flows.

This is where organizations encounter the same operational challenges they face with any application:

• Why does response time degrade during peak usage?
• Which AI use cases are delivering value?
• Where is adoption lagging across the organization?
• Who is using Shadow AI?
• How do we detect when AI‑driven workflows drift from expected behavior?

These are not data science questions. They are operational ones.

As AI becomes business‑critical, it must be managed with the same discipline as other applications. AI should be no exception.

AI Operational Assurance

Many organizations lack the visibility required to operate AI confidently in production. They often have limited insight into performance, cost efficiency, adoption, and risk.

Â鶹ѧÉúľ«Ć·°ć AI Assurance addresses this gap by providing operational visibility into how AI behaves in real environments. It helps organizations answer questions such as:

• Is AI delivering consistent performance as usage scales?
• Are inference costs aligned with business value?
• Is AI being used across the organization?

By connecting AI behavior to operational and business outcomes, AI Assurance moves AI out of isolated experimentation and into a governable part of the enterprise stack.

Accuracy Is the Starting Line, Not the Finish

Model accuracy will always matter. But as AI becomes embedded in daily operations, organizations must look beyond correctness to understand performance, cost, adoption, and impact. Success is not solely defined by whether AI produces the right answer; it’s also defined by whether the system performs reliably, scales sustainably, and delivers expected value.

Â鶹ѧÉúľ«Ć·°ć AI Assurance provides the framework to move AI from experimentation to a system organizations can trust and scale.

Monitoring (Ăśberwachung) ist das Erfassen und Analysieren von Daten aus Netzwerken, Anwendungen, Infrastruktur und dem Nutzererlebnis, um Probleme oder Anomalien zu erkennen. Beim Monitoring werden typischerweise Schwellwertalarme festgelegt, um Betreiber oder Entwickler zu benachrichtigen, wenn Fehler auftreten. Das Ziel des Monitorings besteht darin, Einblicke in die VerfĂĽgbarkeit, Performance und Auslastung zu gewinnen.

Observability (Transparenz) geht einen Schritt weiter, denn sie beschäftigt sich mit dem Verständnis der internen Funktionsweise eines Systems, anstatt nur seine Eingaben und Ausgaben zu überwachen. Observability beinhaltet das Erfassen und Analysieren von Daten auf einer tieferen Ebene und erfordert hochpräzise bereichsübergreifende Daten, um einen ganzheitlichen Blick auf das Systemverhalten zu erhalten. Das Ziel von Observability besteht darin, die proaktive Erkennung und Behebung von Problemen zu ermöglichen, anstatt erst im Nachhinein darauf zu reagieren.

Kurz gesagt, sind Observability und Monitoring wie zwei Seiten derselben Medaille. Monitoring bietet grundlegende Einblicke in ein System, um eine übergeordnete Ansicht des Performance-Verhaltens zu erreichen. Observability geht einen Schritt weiter, denn sie betont die Notwendigkeit, die interne Funktionsweise eines Systems zu verstehen, um seine Performance und Zuverlässigkeit zu verbessern.

Was ist Observability?

Observability ist ein Konzept, das in verschiedenen Bereichen verwendet wird, unter anderem in der Technik, Informatik und Systemanalyse. Sie ist die Fähigkeit, die interne Funktionsweise eines Systems oder Prozesses anhand seiner produzierten Daten und Informationen zu verstehen und zu analysieren. Im Wesentlichen entspricht sie dem Grad, zu dem beobachtet und gemessen werden kann, was in einem System vorgeht.

In der Informatik ist Observability oft mit der Software- und Anwendungsentwicklung verbunden. Sie beinhaltet die Fähigkeit, komplexe Softwaresysteme zu überwachen und Fehler zu beheben, indem Daten aus verschiedenen Quellen erfasst werden, darunter Anwendungsprotokolle, Kennzahlen und Verlaufsinformationen. Dadurch können Entwickler Probleme in der Software identifizieren und beheben, um ihre gesamte Qualität und Performance zu verbessern.

Â鶹ѧÉúľ«Ć·°ć Unified Observability von Â鶹ѧÉúľ«Ć·°ć weitet das Konzept der Observability auf alle IT-Systeme aus, einschlieĂźlich Netzwerk, Infrastruktur, Anwendungen und Nutzererlebnis. Die Lösung nutzt hochpräzise Daten, Analysen, Korrelationen und intelligente Automatisierung, um kontextuelle Daten zu sammeln, die eine schnelle Identifizierung und Behebung von Performance- und Sicherheitsproblemen ermöglichen.

Observability ist ein wichtiges Konzept, mit dem wir Einblicke in die interne Funktionsweise von komplexen Systemen und Prozessen gewinnen können, um ihre Performance, Zuverlässigkeit und gesamte Effektivität zu verbessern.

Was ist Monitoring?

Performance-Monitoring ist das Nachverfolgen und Analysieren der Performance-Kennzahlen eines Systems oder Prozesses, beispielsweise eines Computers, Netzwerks oder einer Anwendung, um sicherzustellen, dass die erforderlichen Performance-Levels oder SLAs (Service Level Agreements) erfĂĽllt werden. Dabei werden verschiedene Kennzahlen, wie Antwortzeit, Durchsatz und Fehlerraten, ĂĽberwacht und mit vorab definierten Benchmarks oder Schwellwerten abgeglichen.

Das Ziel von Performance-Monitoring besteht darin, Performance-Probleme, wie langsame Antwortzeiten, hohe Ressourcenauslastung oder Systemabstürze, zu identifizieren und zu diagnostizieren, um dann geeignete Korrekturmaßnahmen zu ergreifen. Dazu können Systemkonfigurationen angepasst, Hardware- und Softwarekomponenten aktualisiert oder Code und Algorithmen optimiert werden.

Performance-Monitoring spielt eine wesentliche Rolle für die effiziente und effektive Funktionsweise von Systemen und Prozessen sowie die nachhaltige Kundenzufriedenheit und Business Continuity. Es wird häufig in Branchen wie IT, Telekommunikation, Finanzwesen, Gesundheitswesen und Fertigung verwendet, um die Performance von wichtigen Systemen und Anwendungen zu überwachen und zu optimieren.

Observability und Monitoring: Was ist der Unterschied?

Observability und Monitoring sind wichtige Konzepte in der IT mit etwas unterschiedlichen Bedeutungen.

Monitoring ist im Allgemeinen der Prozess zum Erfassen von Systemdaten, wie Performance, VerfĂĽgbarkeit und Auslastung, und zum Identifizieren und Diagnostizieren von Problemen oder Optimieren der Performance anhand dieser Daten. Monitoring erfolgt typischerweise mit einer speziellen Telemetrie, die Daten aus verschiedenen Quellen, beispielsweise aus dem Netzwerk oder Anwendungen, erfasst und analysiert.

Observability ist hingegen ein ganzheitlicheres Konzept für die Fähigkeit, das Verhalten und die Performance eines Systems anhand seiner Ausgaben zu verstehen und zu bewerten. Ein solches System liefert ausreichende Informationen, damit IT-Teams sein Verhalten besser verstehen und Probleme einfacher diagnostizieren können. Typischerweise bietet es eine klar definierte Oberfläche, die es IT-Teams ermöglicht, Daten zu seinem Verhalten zu sammeln und zu analysieren.

Monitoring ist also eine Komponente von Observability zum Sammeln von Systemdaten, während Observability die Fähigkeit ist, das System anhand seiner ausgegebenen Daten zu bewerten.

Welche Vorteile hat Observability?

Observability bietet verschiedene Vorteile, darunter:

1. Schnellere Problemerkennung: Mit Observability können Probleme einfacher in ihrer Entstehung erkannt werden, anstatt auf Benutzerbeschwerden oder Ausfälle zu warten. Dadurch kann die Ausfallzeit reduziert und die gesamte Zuverlässigkeit verbessert werden.
2. Schnellere Problembehebung: Wenn ein Problem erkannt wurde, können Observability-Tools dabei helfen, die genaue Problemursache festzustellen. Â鶹ѧÉúľ«Ć·°ć Unified Observability verwendet intelligente Automatisierung, um nĂĽtzliche Hinweise und Kontext zu sammeln. Das beschleunigt die Problembehebung und Systemwiederherstellung.
3. Höhere Performance: Durch das Monitoring von wichtigen Kennzahlen und Indikatoren kann Observability dazu beitragen, Bereiche mit unzureichender Performance zu identifizieren. Damit können die Performance von Netzwerken und Anwendungen sowie das Nutzererlebnis verbessert werden und potenzielle Probleme vor ihrem Auftreten verhindert werden.
4. Bessere Zusammenarbeit: Observability-Tools können verschiedenen Teams einer Organisation Einblicke in den internen Zustand eines Systems verschaffen. Das kann die Zusammenarbeit zwischen den Teams verbessern, damit alle auf das gemeinsame Ziel einer besseren Performance und Zuverlässigkeit hinarbeiten.
5. Besseres Kundenerlebnis: Observability verbessert das digitale Nutzererlebnis, indem Probleme schneller erkannt und behoben werden, und fĂĽhrt so zu einer steigenden Kundenzufriedenheit und -treue.

Was ist Â鶹ѧÉúľ«Ć·°ć Unified Observability?

Â鶹ѧÉúľ«Ć·°ć IQ ist ein SaaS-basierter Unified Observability-Service, der folgenschwere Probleme aufdeckt und den erforderlichen Kontext liefert, um sie schnell zu lösen. Er verwendet wichtige Kennzahlen von umfassenden Monitoring-Telemetriedaten – von Netzwerken, Infrastrukturen, Anwendungen und Benutzern –, um die Basis von Unified Observability zu schaffen. Der Service setzt vielfältige Analysen ein und korreliert die Daten in fĂĽnf Dimensionen, um verwandte Indikatoren zu einem einzelnen Vorfall zusammenzufassen und präzise Warnungen mit schneller Problemidentifizierung zu ermöglichen. Dann verwendet er intelligente Automatisierungen, die Best Practices von IT-Experten replizieren, um Informationen zu sammeln, den Kontext darzustellen und Prioritäten zu setzen. Dadurch kann die IT Probleme schneller und effizienter beheben.

Klicken Sie hier fĂĽr weitere Informationen zu Â鶹ѧÉúľ«Ć·°ć Unified Observability und Monitoring.