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Embrace Launches Network Watchdog

Embrace launched Network Watchdog, a network monitoring and reporting solution to provide its customers with transparency into application issues.

By using Watchdog by Embrace, the endpoint is measured by how the mobile users' experience the app, and errors and issues are uncovered that cannot be detected from the server-side.

Embrace's new networking dashboard details a mobile app's networking performance, all in one place, surfacing top erroring and slowest calls from all the app's APIs and third-party SDKs. Customers can easily click into any path to see all the users and sessions affected by those calls. Detailed session timelines reproduce exactly where failed calls occur in the user's experience, and whether that user was noticeably affected.

Key features of Embrace's Network Monitoring Dashboards include:

- The ability to solve unresolved crashes for which a trace is not helpful but every single network call would resolve.

- Unsampled tracking of every single network call whether 1st-party API, CDN, or 3rd-party SDK.

- All network errors before they even reach the server so that no error is dropped.

- Automatic insights into which calls most frequently error or are slow to help prioritize which issues to resolve first.

- Clickable workflows into any domain to view every single path belonging to that domain and all the network calls in a given session.

- Alerts by Slack and email on spikes in network errors to catch network outages before end users do. Know when an API changes or errors or a vendor, especially attribution or analytics, is down.

More than 15% of users have suboptimal experiences directly tied to app performance, and while many users complain of “crashes,” almost all of these issues are not true app crashes. Embrace's platform analyzes and replays the details of every single user session to more accurately and quickly detect, diagnose and resolve any user-impacting issues. The platform typically uncovers 5X more issues than previously known by mobile teams and app developers.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

Embrace Launches Network Watchdog

Embrace launched Network Watchdog, a network monitoring and reporting solution to provide its customers with transparency into application issues.

By using Watchdog by Embrace, the endpoint is measured by how the mobile users' experience the app, and errors and issues are uncovered that cannot be detected from the server-side.

Embrace's new networking dashboard details a mobile app's networking performance, all in one place, surfacing top erroring and slowest calls from all the app's APIs and third-party SDKs. Customers can easily click into any path to see all the users and sessions affected by those calls. Detailed session timelines reproduce exactly where failed calls occur in the user's experience, and whether that user was noticeably affected.

Key features of Embrace's Network Monitoring Dashboards include:

- The ability to solve unresolved crashes for which a trace is not helpful but every single network call would resolve.

- Unsampled tracking of every single network call whether 1st-party API, CDN, or 3rd-party SDK.

- All network errors before they even reach the server so that no error is dropped.

- Automatic insights into which calls most frequently error or are slow to help prioritize which issues to resolve first.

- Clickable workflows into any domain to view every single path belonging to that domain and all the network calls in a given session.

- Alerts by Slack and email on spikes in network errors to catch network outages before end users do. Know when an API changes or errors or a vendor, especially attribution or analytics, is down.

More than 15% of users have suboptimal experiences directly tied to app performance, and while many users complain of “crashes,” almost all of these issues are not true app crashes. Embrace's platform analyzes and replays the details of every single user session to more accurately and quickly detect, diagnose and resolve any user-impacting issues. The platform typically uncovers 5X more issues than previously known by mobile teams and app developers.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...