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.

La surveillance consiste à collecter et à analyser les données relatives au réseau, aux applications, à l’infrastructure et à l’expérience utilisateur afin de détecter des problèmes ou des anomalies. La surveillance implique généralement la mise en place de seuils d’alerte pour avertir les opérateurs ou les développeurs en cas de problème. L’objectif de la surveillance est de fournir des informations sur la disponibilité, les performances et l’utilisation.

L’observabilité pousse la surveillance un peu plus loin en soulignant l’importance de comprendre le fonctionnement interne d’un système, au lieu de se contenter de contrôler ses entrées et sorties. L’observabilité implique la collecte et l’analyse de données à un niveau plus approfondi et nécessite des données haute fidélité multi-domaines afin d’obtenir une vision globale du comportement du système. L’objectif de l’observabilité est de permettre une détection et une résolution proactives des problèmes, plutôt qu’une simple résolution réactive.

En bref, l’observabilité et la surveillance représentent deux faces d’une même pièce. La surveillance fournit un niveau de visibilité de base sur le système, tandis que la visibilité vise à dégager une vue plus complète des performances. L’observabilité va encore plus loin en soulignant la nécessité de comprendre le fonctionnement interne d’un système afin d’en améliorer les performances et la fiabilité globales.

Qu’est-ce que l’observabilité ?

L’observabilité est un concept utilisé dans divers domaines, notamment l’ingénierie, l’informatique et l’analyse des systèmes. Il s’agit de la capacité à comprendre et à analyser le fonctionnement interne d’un système ou d’un processus sur la base des données et des informations qu’il produit. Il correspond essentiellement à la mesure dans laquelle nous pouvons observer et mesurer ce qui se passe dans un système.

En informatique, l’observabilité est souvent associée au développement de logiciels et d’applications. Il s’agit de la capacité à surveiller et à déboguer des systèmes logiciels complexes en collectant et en analysant des données provenant de diverses sources, telles que les journaux d’application, les metrics et les traces. Ce faisant, les développeurs peuvent identifier et résoudre les problèmes au sein du logiciel et améliorer sa qualité et ses performances globales.

L’observabilitĂ© unifiĂ©e Â鶹ѧÉúľ«Ć·°ć de Â鶹ѧÉúľ«Ć·°ć Ă©tend le concept d’observabilitĂ© Ă  tous les systèmes IT, y compris le rĂ©seau, l’infrastructure, les applications et l’expĂ©rience utilisateur. Elle s’appuie sur des donnĂ©es, des analyses et des corrĂ©lations haute fidĂ©litĂ©, ainsi que sur l’automatisation intelligente pour recueillir les donnĂ©es contextuelles qui permettent d’identifier et de rĂ©soudre rapidement les problèmes de performance et de sĂ©curitĂ©.

Globalement, l’observabilité est un concept crucial qui nous permet de mieux comprendre le fonctionnement interne des systèmes et processus complexes, ce qui peut nous aider à améliorer leurs performances, leur fiabilité et leur efficacité globale.

Qu’est-ce que la surveillance ?

La surveillance des performances est le processus de suivi et d’analyse des metrics de performance d’un système ou d’un processus, tel qu’un système informatique, un réseau ou une application, afin de s’assurer qu’il respecte les niveaux de performance requis ou les accords de niveaux de service (SLA). Il s’agit de surveiller divers metrics, comme le temps de réponse, le débit et les taux d’erreur, et de les comparer à des valeurs de référence ou à des seuils prédéterminés.

L’objectif de la surveillance des performances est d’identifier et de diagnostiquer les problèmes de performances, tels que des temps de réponse lents, une utilisation élevée des ressources ou des pannes système, et de prendre les mesures appropriées pour les résoudre. Il peut s’agir d’ajuster les configurations système, de mettre à niveau les composants matériels ou logiciels, ou d’optimiser le code ou les algorithmes.

La surveillance des performances est essentielle pour garantir le bon fonctionnement des systèmes et des processus, ainsi que pour assurer la satisfaction des clients et maintenir la continuité de l’activité. Elle est couramment utilisée dans des secteurs tels que l’IT, les télécommunications, la finance, les soins de santé et la fabrication pour superviser et optimiser les performances des systèmes et applications stratégiques.

Observabilité et surveillance : quelle est la différence ?

L’observabilité et la surveillance sont deux concepts importants dans les opérations IT, mais ils ont des significations légèrement différentes.

La surveillance fait généralement référence au processus de collecte de données sur un système, telles que ses performances, sa disponibilité et son utilisation, et à l’utilisation de ces données pour identifier et diagnostiquer les problèmes ou pour optimiser les performances. La surveillance s’effectue généralement à l’aide d’une télémétrie spécialisée qui collecte et analyse des données provenant de diverses sources, telles que le réseau ou les applications.

L’observabilité, quant à elle, est un concept plus global qui renvoie à la capacité de comprendre et de décortiquer le comportement et les performances d’un système à partir des données qu’il produit. Un système observable est un système qui fournit suffisamment d’informations pour permettre au service IT de comprendre son comportement et de diagnostiquer plus facilement les problèmes. Il possède généralement une interface bien définie qui permet au service IT de collecter et d’analyser des données sur son comportement.

En résumé, la surveillance est un sous-ensemble de l’observabilité, la surveillance étant un moyen de collecter des données sur un système, tandis que l’observabilité est la capacité de décortiquer ce système à partir de ses sorties de données.

Quels sont les bénéfices de l’observabilité ?

L’observabilité présente plusieurs bénéfices, notamment :

1. Détection plus rapide des problèmes : Grâce à l’observabilité, il devient plus facile de détecter les problèmes au fur et à mesure qu’ils se produisent, plutôt que d’attendre les plaintes des utilisateurs ou les défaillances. Cela permet de réduire les interruptions et d’améliorer la fiabilité globale.
2. RĂ©solution plus rapide des problèmes : Lorsqu’un problème est dĂ©tectĂ©, les outils d’observabilitĂ© peuvent aider Ă  identifier l’origine du problème. L’observabilitĂ© unifiĂ©e Â鶹ѧÉúľ«Ć·°ć utilise une automatisation intelligente pour rassembler les preuves et le contexte. Cela permet de rĂ©duire le temps nĂ©cessaire Ă  la rĂ©solution du problème et Ă  la remise en service du système.
3. Performances supérieures : En surveillant les mesures et les indicateurs clés, l’observabilité peut aider à identifier les domaines de performance qui ne sont pas optimaux. Cela permet d’améliorer les performances des réseaux, des applications et de l’expérience utilisateur, et de prévenir les problèmes potentiels avant qu’ils ne surviennent.
4. Amélioration de la collaboration : Les outils d’observabilité peuvent fournir une visibilité sur l’état interne d’un système à plusieurs équipes au sein d’une organisation. Ils contribuent à améliorer la collaboration entre les équipes et aident tout un chacun à œuvrer vers un objectif commun d’amélioration des performances et de la fiabilité.
5. Meilleure expérience pour les clients : En détectant et en résolvant les problèmes plus rapidement, l’observabilité peut contribuer à améliorer l’expérience digitale des utilisateurs, ce qui se traduit par une satisfaction et une fidélité accrues des clients.

Qu’est-ce que l’observabilitĂ© unifiĂ©e Â鶹ѧÉúľ«Ć·°ć ?

Â鶹ѧÉúľ«Ć·°ć IQ, service d’observabilitĂ© unifiĂ©e fourni par SaaS, fait ressortir les problèmes ayant le plus d’impact avec leur contexte afin de les rĂ©soudre rapidement. Il s’appuie sur les mesures clĂ©s d’une gamme complète de tĂ©lĂ©mĂ©trie de surveillance (du rĂ©seau, de l’infrastructure, des applications et des utilisateurs finaux) pour fournir les bases d’une observabilitĂ© unifiĂ©e. Il applique une multitude d’analyses et de corrĂ©lations en cinq dimensions afin de regrouper les indicateurs connexes en un seul incident, menant Ă  des alertes plus prĂ©cises et une identification plus rapide des problèmes. Il utilise ensuite l’automatisation intelligente qui reproduit les bonnes pratiques des experts IT afin de recueillir des preuves, Ă©tablir un contexte et dĂ©finir des prioritĂ©s. Le service IT peut ainsi rĂ©soudre les problèmes plus rapidement et plus efficacement.

Pour plus d’informations sur l’observabilitĂ© unifiĂ©e Â鶹ѧÉúľ«Ć·°ć et la surveillance, cliquez ici.