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4 “No Fail” Best Practices for Enhanced Application Diagnostics

Many things can, and will, go wrong during the development of an enterprise application. These issues underscore the importance of using test cycles to detect potential performance-robbing defects before the application is moved into production. To combat the myriad problems waiting to plague the application lifecycle, developers need to equip themselves with items they can employ to troubleshoot such issues when they arise.

User Acceptance Testing, in theory, is used to utilize the end-user to test the application before it moves on to other stages of the application lifecycle and give their approval, but rarely does an application become exposed to the variety of situations it will experience in production.

Too often, the underlying middleware message layer is often regarded as a “black-box” during this process. And that’s ok, as long as there are no problems. Testers know how long a message or transaction took to transit that layer or the architecture. But if that took too long or was routed to the wrong location, they don’t know why.

This lack of visibility can make it rather difficult to reproduce and then resolve a production problem. Lack of visibility also forces development to manually contact the middleware administrator in shared services and request information about message contents. Certainly, this is an interruption to the middleware administrator and a very inefficient, costly and error-prone process.

As more firms move to a DevOps culture, cooperation in usage of tools across development and production is important. At the very least it gets the two teams speaking the same language, which means time spent in trying to reproduce a problem that is adequately specified can be reduced. At best it can help the joint teams rapidly identify a problem, reproduce it in the test cycle and then develop a resolution.

The following “no fail” best practices are designed for Independent Software Vendors to enforce consistent guidelines for application, middleware and transaction diagnostics in order to rapidly identify, trace, replicate and resolve issues that occur during production.

1. Visibility

Ensure that you have the most detailed visibility into the performance of your applications as possible. Synthetic transactions are not enough. Detailed diagnostics down to the message contents or method level are essential -- you need to see more than just what is being passed into and out of an application as if it were a “black box.”

Instead, ensure you have full visibility of each message and transaction. Use diagnostics at each juncture to proactively provide detailed information when an application’s behavior veers from the expected.

2. Traceability

Knowing when a metric has been breached is an important first step in optimizing application performance during test cycles. Knowing exactly what caused the problem is more challenging. Traditional testing methodologies treat the symptoms looking outside-in, not the root cause which often requires an inside-out viewpoint. Make certain that you can trace the message path in its entirety to uncover the precise moment and environment when the problem occurred.

3. Reproducibility

The key to any successful testing program is the ability to reproduce an error. It is the confirmation of a problem solved, and guarantees that the same problem will never need to be resolved twice.

4. Actionability

Once a problem and its trigger have been identified, and after it has been successfully isolated through replication, developers have all of the tools they need to confidently act on the information and permanently resolve application performance problems. This means they need the tools to -- on their own -- create new messages, re-route them and test their problem resolution.

The ability to identify problems sooner in the application lifecycle will yield better results when the need to remediate issues arises in production. This can only happen when development and production are working together as a team, utilizing a common tool set and when development is enabled with full visibility. This approach will save time and money, as well as helping organizations meet SLAs and drive ROI from these applications.

About Charley Rich

Charley Rich, Vice President of Product Management and Marketing at Nastel, is a software product management professional who brings over 20 years of experience working with large-scale customers to meet their application and systems management requirements. Earlier in his career he held positions in Worldwide Product Management at IBM, as Director of Product Management at EMC/SMARTS, and Vice President of Field Marketing for eCommerce firm InterWorld. Charley is a sought after speaker and a published author with a patent in the application management field.

Related Links:

www.nastel.com

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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4 “No Fail” Best Practices for Enhanced Application Diagnostics

Many things can, and will, go wrong during the development of an enterprise application. These issues underscore the importance of using test cycles to detect potential performance-robbing defects before the application is moved into production. To combat the myriad problems waiting to plague the application lifecycle, developers need to equip themselves with items they can employ to troubleshoot such issues when they arise.

User Acceptance Testing, in theory, is used to utilize the end-user to test the application before it moves on to other stages of the application lifecycle and give their approval, but rarely does an application become exposed to the variety of situations it will experience in production.

Too often, the underlying middleware message layer is often regarded as a “black-box” during this process. And that’s ok, as long as there are no problems. Testers know how long a message or transaction took to transit that layer or the architecture. But if that took too long or was routed to the wrong location, they don’t know why.

This lack of visibility can make it rather difficult to reproduce and then resolve a production problem. Lack of visibility also forces development to manually contact the middleware administrator in shared services and request information about message contents. Certainly, this is an interruption to the middleware administrator and a very inefficient, costly and error-prone process.

As more firms move to a DevOps culture, cooperation in usage of tools across development and production is important. At the very least it gets the two teams speaking the same language, which means time spent in trying to reproduce a problem that is adequately specified can be reduced. At best it can help the joint teams rapidly identify a problem, reproduce it in the test cycle and then develop a resolution.

The following “no fail” best practices are designed for Independent Software Vendors to enforce consistent guidelines for application, middleware and transaction diagnostics in order to rapidly identify, trace, replicate and resolve issues that occur during production.

1. Visibility

Ensure that you have the most detailed visibility into the performance of your applications as possible. Synthetic transactions are not enough. Detailed diagnostics down to the message contents or method level are essential -- you need to see more than just what is being passed into and out of an application as if it were a “black box.”

Instead, ensure you have full visibility of each message and transaction. Use diagnostics at each juncture to proactively provide detailed information when an application’s behavior veers from the expected.

2. Traceability

Knowing when a metric has been breached is an important first step in optimizing application performance during test cycles. Knowing exactly what caused the problem is more challenging. Traditional testing methodologies treat the symptoms looking outside-in, not the root cause which often requires an inside-out viewpoint. Make certain that you can trace the message path in its entirety to uncover the precise moment and environment when the problem occurred.

3. Reproducibility

The key to any successful testing program is the ability to reproduce an error. It is the confirmation of a problem solved, and guarantees that the same problem will never need to be resolved twice.

4. Actionability

Once a problem and its trigger have been identified, and after it has been successfully isolated through replication, developers have all of the tools they need to confidently act on the information and permanently resolve application performance problems. This means they need the tools to -- on their own -- create new messages, re-route them and test their problem resolution.

The ability to identify problems sooner in the application lifecycle will yield better results when the need to remediate issues arises in production. This can only happen when development and production are working together as a team, utilizing a common tool set and when development is enabled with full visibility. This approach will save time and money, as well as helping organizations meet SLAs and drive ROI from these applications.

About Charley Rich

Charley Rich, Vice President of Product Management and Marketing at Nastel, is a software product management professional who brings over 20 years of experience working with large-scale customers to meet their application and systems management requirements. Earlier in his career he held positions in Worldwide Product Management at IBM, as Director of Product Management at EMC/SMARTS, and Vice President of Field Marketing for eCommerce firm InterWorld. Charley is a sought after speaker and a published author with a patent in the application management field.

Related Links:

www.nastel.com

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...