Categories: Technical Deep Dive

A joint post from the teams at NeuBird and Confluent

For organizations running managed Confluent, the company behind Apache Kafka® as a central nervous system for their data, ensuring smooth operations is mission-critical. While Confluent Cloud eliminates much of the operational burden of managing Kafka clusters, application teams still need to monitor and troubleshoot client applications connecting to these clusters.

Traditionally, when issues arise—whether it’s unexpected consumer lag, authorization errors, or connectivity problems—engineers must manually piece together information from multiple observability tools, logs, and metrics to identify root causes. This process is time-consuming, requires specialized expertise, and often extends resolution times.

Today, we’re excited to share how NeuBird’s Hawkeye, a GenAI-powered SRE assistant, is transforming this experience by automating the investigation and resolution of Confluent Cloud incidents—allowing your team to focus on innovation rather than firefighting.

The Foundation: Kafka Client Observability with Confluent

Confluent’s observability setup provides a strong foundation for monitoring Kafka clients connected to Confluent Cloud. It leverages:

• A time-series database (Prometheus) for metrics collection
• Client metrics from Java consumers and producers
• Visualization through Grafana dashboards
• Failure scenarios to learn from and troubleshoot

The demo is incredibly valuable for understanding how to monitor Kafka clients and diagnose common issues, but it still relies on human expertise to interpret the data and determine root causes.

Enhancing the Experience with Kubernetes and AI-driven Automated Incident Response

NeuBird builds on Confluent’s robust observability foundation by integrating Hawkeye, our GenAI-powered SRE, directly into the Kafka monitoring ecosystem. This combination goes beyond monitoring to introduce intelligent, automated incident response, significantly reducing Mean Time to Resolution (MTTR).

Here’s how NeuBird augments Confluent’s observability with three significant improvements:

1. Kubernetes Deployment: We’ve containerized the entire setup and made it deployable on Kubernetes (EKS), making it more representative of production environments and easier to deploy.
2. Alert Manager Integration: We’ve added Prometheus Alert Manager rules that trigger PagerDuty incidents, creating a complete alerting pipeline.
3. Audit Logging: We’ve expanded the telemetry scope to include both metrics and logs in CloudWatch, giving a more comprehensive view of the environment.

Most importantly, we’ve integrated Hawkeye—NeuBird’s GenAI-powered SRE—to automatically investigate and resolve incidents as they occur, significantly reducing Mean Time to Resolution (MTTR).

Seeing it in Action: Authorization Revocation Scenario

Let’s walk through a real-world scenario from the Confluent demo: the “Authorization Revoked” case, where a producer’s permission to write to a topic is unexpectedly revoked.

The Traditional Troubleshooting Workflow

In the original demo workflow, here’s what typically happens:

1. An engineer receives an alert about producer errors
2. They log into Grafana to check producer metrics
3. They notice the Record error rate has increased
4. They check Confluent Cloud metrics and see inbound traffic but no new retained bytes
5. They examine producer logs and find TopicAuthorizationException errors
6. They investigate ACLs and find the producer’s permissions were revoked
7. They restore the correct ACLs to resolve the issue

This manual process might take 15-30 minutes for an experienced Kafka engineer, assuming they’re immediately available when the alert triggers.

The Hawkeye-Automated Workflow

With our enhanced setup including Hawkeye, the workflow is transformed:

1. Prometheus Alert Manager detects increased error rates and triggers a PagerDuty incident
2. Hawkeye automatically begins investigating the issue by:
   • Retrieving and analyzing producer metrics from Prometheus
   • Correlating with Confluent Cloud metrics
   • Examining producer logs for error patterns
   • Checking AWS CloudWatch for audit logs showing ACL changes
3. Within minutes, Hawkeye identifies the TopicAuthorizationException and links it to recent ACL changes
4. Hawkeye generates a detailed root cause analysis with specific remediation steps
5. An engineer reviews Hawkeye’s findings and applies the recommended fix (or optionally, approves Hawkeye to implement the fix automatically)

The entire process is reduced to minutes, even when the issue occurs outside business hours. More importantly, your specialized Kafka engineers can focus on more strategic work rather than routine troubleshooting.

Demo Video

In this video, we demonstrate the complete workflow:

1. How we deploy the enhanced Confluent observability solution to Kubernetes
2. Triggering the authorization revocation scenario
3. Watching Hawkeye automatically detect, investigate, and diagnose the issue
4. Reviewing Hawkeye’s detailed analysis and remediation recommendations
5. Implementing the fix and verifying the resolution

The Technical Architecture

Our enhanced solution builds upon Confluent’s observability foundation with several key components:

• Kubernetes Deployment: All components are packaged as containers and deployed to EKS using Helm charts, making the setup reproducible and scalable.
• Prometheus and Alert Manager: We’ve added custom alerting rules specifically designed for Confluent Cloud metrics and common failure patterns.
• AWS CloudWatch Integration: Both metrics and logs are forwarded to CloudWatch, providing a centralized location for all telemetry data.
• Hawkeye Integration: Hawkeye connects securely to your telemetry sources with read-only permissions, leveraging GenAI to understand patterns, correlate events, and recommend precise solutions.

The architecture respects all security best practices—Hawkeye never stores your telemetry data, operates with minimal permissions, and all analysis happens in ephemeral, isolated environments.

Real-World Impact

Organizations using Hawkeye with Confluent Cloud have seen significant operational improvements:

• Reduced MTTR: Issues that previously took hours to diagnose are now resolved in minutes
• Decreased Alert Fatigue: Engineers are only engaged when human intervention is truly needed
• Knowledge Democratization: Teams less familiar with Kafka can confidently operate complex Confluent Cloud environments
• Improved SLAs: With faster resolution times, application availability and performance metrics improve

As one example, an enterprise IT storage company reduced their MTTR for DevOps pipeline failures by implementing Hawkeye. When experiencing a crash loop with one of their applications causing production downtime, Hawkeye automatically picked up the alert from PagerDuty, investigated the issue, and determined that the crashes were happening due to a recent application deployment. Hawkeye recommended which specific application and process needed to be rolled back, dramatically reducing resolution time.

Getting Started

Want to try this enhanced observability setup with your own Confluent Cloud environment? Here’s how to get started:

1. Start with the original Confluent observability demo to understand the components
2. Check out our GitHub repository for the Kubernetes-ready version with Prometheus Alert Manager rules
3. Schedule a demo to see Hawkeye in action with your Confluent Cloud environment

Conclusion

The combination of Confluent Cloud and Neubird’s Hawkeye represents a powerful shift in how organizations operate Kafka environments. By leveraging Confluent’s rich telemetry data and Hawkeye’s GenAI-powered automation, teams can significantly reduce operational overhead, improve reliability, and focus on delivering value rather than troubleshooting infrastructure.

As data streaming becomes increasingly central to modern applications and with availability of fully managed Kafka and Flink solutions in Confluent Cloud, this type of intelligent automation will be essential for scaling operations teams effectively—letting them support larger, more complex deployments without proportionally increasing headcount or sacrificing reliability.

We’re excited to continue innovating at the intersection of observability, AI, and data streaming. Let us know in the comments how you’re approaching observability for your Confluent Cloud environments!

At DevOpsCon San Diego this year the energy was electric and the message was loud and clear: DevOps teams are navigating relentless operational complexity—and they’re looking for AI that actually works in their world. Not AI that lives in a demo, but intelligent automation that fits securely into hybrid environments, accelerates incident response, and helps engineers focus on what matters most.

Across sessions and conversations, the sentiment was strikingly consistent: teams don’t need more dashboards or alerts—they need fewer manual steps and faster root cause clarity.

AI Is Everywhere—But Pragmatism Is Back

AI agents and GenAI were everywhere at the conference, but the buzz was grounded in real-world need. Sessions underscored a shift in mindset: visibility is important—but insight and action are what actually move the needle.

DevOps professionals weren’t chasing the latest AI trend—they were seeking solutions to their most pressing operational challenges. The conversations I had at our booth consistently returned to one theme: how can AI help us work smarter, not harder?

On-Call Burnout Is Boiling Over

Incident response continues to drain DevOps teams. From late-night pings to hours spent tracing pipelines and logs, on-call has become more tedious and time-consuming—even as tooling has improved.

Teams are exhausted from stitching together fragmented telemetry. What they want is AI that understands their stack, integrates into existing systems, and helps get to the root cause faster—without adding another portal or platform to manage.

From Curiosity to Critical Path

Many teams shared past experiments with AI—mostly chatbots or copilots for ticketing or knowledge lookups. Useful, but shallow. Now, the question is different: “Can AI investigate incidents in our production environment without exposing our data?”

Security was a recurring theme. Multiple teams had tried sending telemetry into public LLMs and quickly rolled it back.

One CTO summed it up perfectly: “Dumping production logs into a public LLM isn’t innovation—it’s a liability.”

Sessions that explored successful AI implementation, like Justin Griffin’s real-world story of speeding up deployment investigations with an AI agent, sparked important discussions. During the Q&A, a recurring theme emerged from the audience: teams desperately want AI that can connect the dots between different failure points without requiring them to manually correlate data across multiple tools. As the session demonstrated, the value comes from combining reasoning with context—and doing it securely.

The Security-First AI Revolution

What struck me most about DevOpsCon 2025 was how security considerations are driving better AI adoption, not hindering it. Organizations have learned from early missteps and are now demanding enterprise-grade solutions.

Teams shared cautionary tales of experimenting with general-purpose LLMs—from hallucinated recommendations that caused production outages to security breaches from exposing sensitive telemetry data. The lesson is clear: enterprise operations require purpose-built AI agents, not retrofitted consumer tools.

The Path Forward: Secure, Embedded, Purpose-Built AI

DevOps teams aren’t looking for bolt-on bots or generic copilots. They’re demanding intelligent agents that can integrate deeply with their observability and CI/CD systems, run securely in hybrid environments, and reason through telemetry rather than just summarize it.

That’s why interest in Hawkeye surged at our booth. Teams saw how it can operate in enterprise environments– cloud-native, on-prem and in hybrid cloud- using chain-of-thought workflows to surface root causes from real telemetry—without ever exposing sensitive data outside of their control.

DevOps Isn’t Getting Simpler—But Your Workflow Can

DevOpsCon 2025 made one thing clear: tool fatigue is real, alert overload is unsustainable, and AI has a critical role to play in restoring signal, trust, and speed.

Engineers aren’t asking AI to replace them. They’re asking for AI that thinks like an expert, works with them, and reduces the operational noise.

If that’s what your team is ready for, let’s connect. 👉 Book a demo to see how Hawkeye helps reduce MTTR, eliminate redundant work, and bring calm back to your on-call.

In the rapidly evolving landscape of IT operations, artificial intelligence has emerged as a powerful force for managing complex systems. However, with this power comes a critical challenge: building and maintaining trust. At Neubird, we recognize that trust isn’t just about powerful technology—it’s about transparency, accountability, and consistent results. Let’s explore how Hawkeye’s approach to transparent AI operations is setting new standards in the industry.

For a full breakdown of how Hawkeye works, check out our deep dive blog.

The Trust Challenge in AI Operations

Traditional IT operations rely on human-readable logs, clear audit trails, and well-documented processes. When introducing AI into this environment, maintaining this transparency becomes both more crucial and more challenging. Engineers need to understand not just what actions were taken, but why they were chosen and how decisions were made.

Hawkeye’s Pillars of Transparent Operations

• Explainable Decision Making

At every step of an investigation, Hawkeye maintains clear documentation of its reasoning process. Unlike black-box AI systems that simply provide conclusions, Hawkeye shows its work:

– Detailed investigation plans based on historical patterns

– Clear documentation of data sources consulted

– Step-by-step reasoning for conclusions drawn

– Evidence-based recommendations with supporting data

• Comprehensive Audit Trails

In IT operations, accountability is non-negotiable. Hawkeye maintains detailed audit trails that track:

– Every investigation step taken

– Data sources accessed and queries executed

– Decision points and their rationale

– Recommended actions and their expected outcomes

These audit trails serve multiple purposes: they provide accountability, enable learning from past incidents, and help teams understand how Hawkeye adapts its approach over time.

• Human Oversight and Control

While Hawkeye is powerful, it’s designed to augment human expertise, not replace it. Key aspects of this approach include:

– Customer-controlled access policies that can be revoked instantly

– Read-only operations by default, ensuring system safety

– Clear presentation of evidence for human validation

– Ability to adjust investigation parameters based on human input

The Role of Architecture in Trust

Hawkeye’s commitment to transparency isn’t just about features—it’s embedded in its architecture:

 Secure by Design

– Zero data storage policy ensures privacy

– Ephemeral processing protects sensitive information

– Read-only access prevents unauthorized changes

Verifiable Processing

The system’s telemetry program generation creates a clear chain of evidence:

– Programs are generated using controlled, fine-tuned LLMs

– Processing occurs in isolated memory spaces

– Results are consistently formatted and verifiable

– All data handling is traceable and auditable

Real-World Impact: From Trust to Value

The transparency built into Hawkeye creates a virtuous cycle:

1. Clear evidence builds confidence in AI-driven decisions
2. Understanding leads to better collaboration between AI and engineers
3. Traceable outcomes enable continuous improvement
4. Trust enables broader adoption and more valuable automation

 The Future of Transparent AI Operations

As AI continues to transform IT operations, transparency will become even more critical. Hawkeye’s approach demonstrates how AI can be both powerful and trustworthy, setting a new standard for the industry.

Traditional IT workflows are time-consuming and involve constant context switching. Engineers spend hours manually investigating alerts and correlating events before taking action.

• Traditional SRE Workflow:

1. Alert fires
2. Check CloudWatch
3. Open ServiceNow
4. Investigate logs
5. Correlate events
6. Document findings
7. Take action

⏳ Time spent: Hours
🔄 Context switches: 15+

With Hawkeye, this workflow is transformed into an AI-driven process that reduces manual effort while maintaining transparency and accountability.

• Modern SRE Workflow with GenAI:

1. AI correlates data
2. Reviews root cause
3. Implements solution

⏳ Time spent: Minutes
🔄 Context switches: 1

By using generative AI, Hawkeye reduces operational noise, streamlines investigations, and allows teams to focus on higher-level strategic tasks instead of repetitive workflows.

Ream more: Power-up your AWS CloudWatch and ServiceNow SRE workflows

Conclusion

In today’s business environment, trust isn’t optional—it’s essential. Hawkeye’s commitment to transparency, from its architecture to its outputs, ensures that teams can confidently embrace AI-driven operations while maintaining the accountability their organizations require.

The future of IT operations will be defined not just by what AI can do, but by how well it can be understood and trusted. Through its innovative approach to transparency, Hawkeye is helping shape that future today, enabling teams to build reliable, scalable, and trustworthy AI-powered operations.

To see Hawkeye in action and understand how it can elevate your IT operations, book a demo today and experience the future of trustworthy AI firsthand.

How VDI Teams Are Shaking Up Their VDI Management & Operations with AI-Powered Analysis

Monday morning hits, and your VDI team is staring down dozens of ControlUp alerts about crummy user experience scores. Admins start digging into metrics, comparing performance data across virtual desktop sessions, trying to figure out if the root cause is the host, the network, or something else entirely.

Meanwhile, users are complaining about sluggish performance, and productivity takes a nosedive.
This happens all the time as VDI teams try to keep desktop performance solid while juggling increasingly complex virtual setups. ControlUp gives you deep visibility, sure, but as environments grow, the sheer amount of data can swamp anyone.

The VDI Monitoring Headache

Today’s virtual desktop environments are trickier than ever, supporting huge numbers of remote users who need a consistently good desktop experience. VDI monitoring tools like ControlUp capture this complexity with detailed metrics across many layers:

• User experience scores
• Application performance metrics
• Resource utilization statistics
• Network latency measurements
• Host and hypervisor metrics
• Login times and session data

While ControlUp is great at collecting and showing this data, VDI teams often find themselves bouncing between views, manually connecting metrics, and spending valuable time just trying to piece together the story behind performance hiccups.

The problem isn’t a lack of data. It’s making sense of it all when there’s so much.

The Limitations of Traditional VDI Monitoring

Even with good VDI monitoring solutions, organizations still hit roadblocks:

• Too many disconnected alerts make it hard to know what’s important.
• Performance data is stuck in silos, needing manual correlation.
• Issues often only get attention after they affect users.
• VDI admins burn hours investigating tricky problems.
• Know-how about specific environment quirks stays stuck in individual admins’ heads.

These limits just get worse as VDI environments grow. When you’re supporting thousands of virtual desktops across different locations, even seasoned admins can get buried in monitoring data.

Meet Hawkeye: Your GenAI-Powered ControlUp VDI Monitoring Analyst

Think about a different way to handle VDI operations. Instead of people trying to swim through this data flood, Hawkeye acts like a smart agent that understands the tangled relationships in your virtual desktop environment. By connecting directly with ControlUp, Hawkeye changes how teams monitor, analyze, and tune their VDI infrastructure.

Hawkeye doesn’t replace your VDI monitoring tools. It makes them way more valuable by applying AI to the data they already gather.

Beyond Just VDI Management

When looking into a VDI incident, Hawkeye does more than just basic metric checks:

• It understands the relationships between hosts, sessions, and applications
• It correlates performance metrics across different infrastructure layers.
• It recognizes patterns in user behavior and resource use.
• It figures out how infrastructure changes affect user experience.
• It spots chances for optimization before users feel the pain.
• It learns from every investigation, building a deep understanding of your specific VDI setup.

This analysis happens in seconds, not the minutes or hours it would take an admin to pull together and process the same info.

The New VDI Performance Management Workflow

Old-school VDI monitoring means admins have to:

• Watch multiple ControlUp dashboards.
• Switch between different metric views constantly.
• Manually correlate performance data.
• Document findings and what they did.
• Hunt down related changes and configs.

With Hawkeye boosting your VDI workflow, admins start with a single view of the problem and all the relevant info needed to fix it. Routine issues come with clear, actionable advice, while complex problems get detailed investigation summaries that already include data from across your VDI environment.

Check out the video below or read more on how to transform VDI login performance with AI.

From Reactive to Proactive VDI Management

Pairing Hawkeye with ControlUp tackles core operational headaches. Finding experienced VDI administrators is tough and costly, and organizations are always under pressure to keep desktop performance up while managing costs.

Hawkeye changes the game by:

• Automating routine investigations and providing intelligent analysis.
• Identifying potential issues before they impact user experience.
• Suggesting proactive optimizations based on usage patterns.
• Building a knowledge base of environment-specific insights.
• Helping new team members get up to speed faster.

The Path Forward: Smarter VDI Optimization

As Hawkeye learns your VDI environment, it moves beyond just fixing problems to proactively optimizing things by:

• Predicting potential performance degradation before users are affected
• Recommending resource allocation adjustments based on usage patterns
• Suggesting configuration improvements for optimal user experience
• Identifying opportunities for infrastructure optimization
• Providing trend analysis for capacity planning

Getting Started

Adding Hawkeye alongside ControlUp is simple. Hawkeye’s integration capabilities mean you can connect it to your entire observability stack, creating a unified intelligence layer across all your tools.

1. Connect Hawkeye to your ControlUp environment
2. Configure access to relevant metrics and logs
3. Begin receiving AI-powered insights and recommendations
4. Watch as Hawkeye learns and adapts to your specific environment

Read more: 

• See how you can transform your Datadog & PagerDuty Workflows
• or power-up your Splunk and PagerDuty SRE workflows

Take the Next Step

Ready to improve how you manage your virtual desktop infrastructure? Check our demo or contact us to learn how Hawkeye can become your team’s AI-powered VDI analyst and help your organization handle the complexity of modern virtual desktop environments.

FAQ

How is VDI different from VM?

A Virtual Machine (VM) is a single virtual computer – it has its own OS, CPU, memory, the works, all running separately on a bigger server. VDI (Virtual Desktop Infrastructure) is the whole system that uses a bunch of those VMs specifically to give users their own virtual desktops over the network. So, a VM is the basic building block; VDI is the setup using those blocks to deliver desktops, managed centrally.

Is a VDI a VPN?

Nope, they’re different tools for different jobs. VDI gives you a complete virtual desktop that lives on a server somewhere else; you just access it remotely. All your data and apps stay on that central server. A VPN, on the other hand, just creates a secure connection (like a private tunnel) from your computer back to your company’s network, letting you access internal stuff as if you were in the office, but using your own device’s OS and apps.

What is the difference between VDI and Citrix?

VDI is the general concept or technology for delivering virtual desktops. Lots of companies offer VDI solutions. Citrix is one of those companies – they make specific products (like Citrix Virtual Apps and Desktops) that are VDI solutions, often adding their own special features for things like app virtualization or making the connection feel smoother.

Is VDI the same as remote desktop?

Not quite. Remote Desktop (like Microsoft’s RDP) often lets you connect to a computer (physical or virtual) that might be shared by multiple people at the same time. Resources get shared, and there’s less isolation. VDI typically gives each user their own dedicated virtual desktop running inside its own VM, usually hosted in a data center. This means it’s more isolated, you can often customize it more, and it’s built for managing lots of users securely.

What is a ControlUp?

ControlUp monitoring tool is used by IT teams to manage virtual desktop environments (VDI), physical desktops, and servers. It gives them a real-time look at performance stuff like how users are experiencing their sessions, how resources are being used, and network lag. It uses agents and monitors to gather this data, feeding it into dashboards and alerts so admins can troubleshoot problems, like why someone’s login is taking forever. Learn more.

What is a ControlUp monitor?

A ControlUp monitor is a component of the ControlUp platform. Its job is to constantly collect performance data in real-time from your VDI, physical desktops, or servers – things like user experience scores or resource usage. It usually runs 24/7 on a dedicated machine and sends all that info back to the main ControlUp dashboards and alerting system so admins can keep an eye on system health. It gives you lots of visibility, but seeing the whole picture from all that data still takes manual work and correlation, unlike AI-driven tools like Hawkeye that automate insights.

What is the ControlUp agent used for?

The ControlUp agent is a small piece of software you install on the actual endpoints – the virtual desktops, physical machines, or servers being monitored. It gathers detailed performance info right from the source (like CPU usage, how apps are running, network latency, etc.) and sends it back to the ControlUp console or monitor. This lets admins troubleshoot problems in real-time, figure out issues like slow logins, and even perform remote actions on the machine.

How SRE teams are transforming VPN troubleshooting with AI

It’s 3 AM, and your monitoring system lights up with alerts about application connectivity issues. The initial investigation shows that traffic is flowing to your VPN interface, but seemingly vanishing into thin air before reaching its destination. Sound familiar? For network engineers and SRE teams, this “black hole” scenario is both common and frustratingly complex to diagnose.

The VPN Black Hole Challenge

Consider this recent scenario: A large e-commerce platform suddenly experienced order processing delays. Their payment service, running in AWS, couldn’t reach the payment processor’s API through a site-to-site VPN. Traffic appeared normal leaving the AWS environment, but never arrived at the destination. The monitoring dashboards showed green – the VPN tunnel was up, routes were in place, and security groups were correctly configured.

Yet the problem persisted. The traditional approach meant multiple teams manually checking:

• VPN tunnel status and metrics
• Route table configurations
• Security group and NACL rules
• BGP session states
• MTU settings across the path
• IPSec phase 1 and 2 configurations
• Dead peer detection (DPD) timeouts

Each team had their own monitoring tools, none of which could correlate data across the entire path. Hours passed before someone noticed that a recent security patch had modified the IPSec transform set on one side of the tunnel, creating a mismatch that dropped packets silently.

Beyond Traditional Monitoring

The challenge isn’t lack of monitoring – it’s that traditional tools can’t connect the dots across complex network paths. Each dashboard shows its piece of the puzzle, but assembling the complete picture requires extensive manual correlation and deep networking expertise.

This is where AI-powered investigation transforms the game. When this same company encountered a similar issue two months later, Hawkeye immediately:

• Correlated VPN metrics from both endpoints
• Detected the asymmetric traffic pattern
• Identified configuration drift between tunnel endpoints
• Pinpointed the exact parameter mismatch
• Provided a clear remediation plan

What previously took hours of manual investigation across multiple teams was resolved in minutes.

The Power of Context-Aware Analysis

Hawkeye’s approach goes beyond simple metric monitoring. By understanding the relationships between network components, it can:

• Track configuration changes across both ends of VPN tunnels
• Correlate routing updates with traffic patterns
• Monitor encryption parameters for mismatches
• Detect subtle patterns in packet loss and latency
• Identify asymmetric routing issues

More importantly, Hawkeye learns from each investigation, building a knowledge base of VPN failure patterns specific to your environment. This means faster resolution times and often, prevention of issues before they impact services.

From Reactive to Proactive

For network teams, this transformation means:

• Fewer middle-of-night emergencies
• Reduced mean time to resolution (MTTR)
• Automated correlation of networking data
• Early warning of potential VPN issues
• More time for strategic network planning

Getting Started

Ready to transform your VPN troubleshooting? Hawkeye integrates with your existing network monitoring tools, including CloudWatch, Azure Monitor, and traditional NMS platforms. By connecting these data sources, you create a unified view of your network infrastructure with intelligent, AI-powered analysis.

Contact us to learn how Hawkeye can become your team’s AI-powered networking expert and help prevent VPN black holes from disrupting your services

How Development Teams Are Conquering Test Log Complexity with GenAI

Picture this: Your CI/CD pipelines are running thousands of tests each month, generating an overwhelming volume of logs. Your development team spends hours sifting through these logs whenever a test fails, trying to piece together what went wrong. With each passing sprint, the challenge only grows as your test suite expands. Sound familiar?

In today’s fast-paced development environment, continuous integration isn’t just about running tests—it’s about quickly understanding and acting on test results. Yet as organizations scale their testing practices, they face a growing challenge: the sheer volume of test logs has become overwhelming. Development teams running thousands of tests monthly find themselves drowning in log data, making it increasingly difficult to maintain velocity while ensuring quality.

This isn’t just about having access to logs. Modern CI/CD platforms provide comprehensive logging capabilities, and most teams have sophisticated test suites in place. The real challenge lies in the time and effort required to analyze these logs effectively. When a critical test fails, engineers often spend hours manually reviewing logs, correlating different test runs, and trying to identify patterns—time that could be better spent on innovation and feature development.

The Hidden Cost of Manual Log Analysis

The traditional approach to handling test failures typically involves:

• Manually searching through logs to identify the point of failure
• Cross-referencing multiple test runs to spot patterns
• Investigating related code changes that might have contributed
• Documenting findings for team knowledge sharing
• Creating tickets for identified issues

This process is not only time-consuming but also prone to human error. Important details can be missed, patterns can go unnoticed, and valuable engineering time is consumed by what is essentially a data analysis problem. The impact on team productivity and morale is significant, with engineers spending more time investigating failures than writing new code.

Enter Hawkeye: Your GenAI Powered SRE for Log Analysis

Consider a fundamentally different approach. Instead of humans trying to process this flood of information, Hawkeye acts as your AI teammate that can instantly analyze thousands of test logs, identify patterns, and provide actionable insights. This isn’t about replacing your existing CI/CD tools—it’s about enhancing them with Hawkeye’s intelligent analysis capabilities that operate at machine scale.

Watch as Hawkeye analyzes complex test failures in real-time, providing immediate insights and actionable recommendations.

When investigating a test failure, Hawkeye provides:

• Immediate correlation of current failures with historical patterns
• Automatic identification of related code changes and commits
• Context-aware analysis that understands your specific testing patterns
• Natural language summaries that make complex issues understandable
• Proactive identification of potential test flakiness

This analysis happens in seconds, not the hours it would take a human engineer to gather and process the same information. More importantly, the AI learns from each investigation, building a deep understanding of your specific testing patterns and common failure modes.

The Transformed Workflow

The transformation in daily operations is profound. Instead of spending hours manually searching through logs, engineers receive comprehensive analysis that includes:

• Root cause identification with supporting evidence
• Historical context for similar failures
• Correlation with recent code changes
• Recommended next steps for resolution
• Patterns that might indicate broader issues

This shifts the engineer’s role from log parser to strategic problem solver, focusing on fixing issues rather than just finding them.

Beyond Log Analysis: Transforming Development Practices

The impact extends far beyond just saving time on log analysis. Teams that adopt Hawkeye often experience:

• Reduced Mean Time to Resolution (MTTR) for test failures
• Improved test suite reliability through better pattern recognition
• Enhanced knowledge sharing across the team
• More time for feature development and innovation
• Better understanding of test suite behavior and patterns

For organizations, this translates to tangible benefits:

• Faster release cycles
• Improved code quality
• Better resource utilization
• Enhanced team satisfaction
• Reduced operational overhead

The Path Forward

As development practices continue to evolve and test suites grow more complex, the traditional approach of manual log analysis becomes increasingly unsustainable. AI-powered analysis represents not just a tool, but a fundamental shift in how teams handle test failures and maintain quality at scale.

By leveraging AI to handle the heavy lifting of log analysis, teams can:

• Focus on strategic problem-solving rather than log parsing
• Identify and address systemic issues more quickly
• Maintain velocity while ensuring quality
• Build more robust and reliable test suites

Getting Started

Implementing Hawkeye alongside your existing CI/CD tools is a straightforward process that begins paying dividends immediately. While this blog focuses on test log analysis, Hawkeye’s capabilities extend to any aspect of your development pipeline that generates logs and requires investigation.

Ready to transform how your team handles test failures? Contact us to learn how Hawkeye can become your AI teammate in conquering test log complexity and accelerating your development pipeline. Our team will work with you to integrate Hawkeye with your existing tools and processes, ensuring a smooth transition to AI-powered log analysis.

How SRE teams are eliminating uneven traffic distribution with Hawkeye

Picture this: Your monitoring dashboard shows healthy instances, your load balancer configurations look correct, yet your application traffic is stubbornly refusing to distribute evenly. Some servers are overwhelmed while others sit nearly idle. Sound familiar? For SRE teams managing cloud-native applications, uneven load distribution isn’t just an annoyance—it’s a critical issue that can impact application performance, cost efficiency, and user experience.

The Hidden Complexity of Modern Load Balancing

Today’s load balancing challenges go far beyond simple round-robin distribution. Modern architectures involve:

• Multiple load balancer tiers (L4/L7)
• Dynamic instance health checks
• Sticky sessions
• Custom routing rules
• Auto-scaling groups
• Cross-zone balancing
• Weighted routing policies

When traffic distribution goes awry, the root cause often lies in the complex interactions between these components. Traditional monitoring tools show you the symptoms—uneven server loads, response time variations, and throughput discrepancies. But pinpointing the exact cause requires correlating data across multiple layers of your infrastructure.

The Traditional Troubleshooting Treadmill

Currently, SRE teams face a time-consuming investigation process:

1. Manually comparing traffic patterns across instances
2. Analyzing load balancer access logs
3. Reviewing health check configurations
4. Checking for network issues
5. Verifying session persistence settings
6. Investigating application-level routing
7. Cross-referencing recent infrastructure changes

This process typically takes hours, requiring deep expertise across networking, application architecture, and cloud services. Meanwhile, the uneven load continues to impact your application’s performance and reliability.

Enter Hawkeye: Your AI-Powered Load Balancing Expert

Hawkeye transforms this investigation process by automatically correlating telemetry data across your entire stack. Instead of manually piecing together the puzzle, Hawkeye’s GenAI capabilities provide immediate insights into load balancing issues:

• Comprehensive Analysis: Hawkeye simultaneously analyzes load balancer metrics, application logs, network flows, and configuration changes to identify patterns and anomalies.
• Root Cause Determination: By understanding the relationships between different components, Hawkeye can quickly identify whether uneven distribution stems from configuration issues, application behavior, network problems, or infrastructure changes.
• Proactive Detection: Hawkeye learns your application’s normal traffic patterns and can alert you to subtle distribution anomalies before they become critical issues.

The Hawkeye Advantage in Action

When investigating load balancing issues, Hawkeye:

1. Automatically correlates metrics across all load balancer tiers
2. Analyzes traffic distribution patterns over time
3. Identifies configuration drift or recent changes
4. Checks for health check inconsistencies
5. Verifies session persistence behavior
6. Examines application-level routing decisions
7. Provides clear, actionable remediation steps

Real Impact on Operations

For SRE teams, this means:

• Reduced MTTR for load balancing issues from hours to minutes
• Fewer false positives from normal traffic variations
• Clear visibility into complex routing behavior
• Proactive detection of potential distribution problems
• More time for strategic infrastructure improvements

Moving Forward: From Reactive to Proactive

The future of load balancer management isn’t about better dashboards—it’s about intelligent analysis that understands your infrastructure’s behavior patterns. Hawkeye represents this shift, serving as an AI teammate that continuously monitors, analyzes, and optimizes your load balancing configuration.

Getting Started

Ready to transform how you manage load balancing? Hawkeye integrates seamlessly with your existing infrastructure:

1. Connect your cloud provider’s load balancer metrics
2. Enable access to configuration and change logs
3. Start receiving intelligent, context-aware analysis

Don’t let uneven load distribution impact your application’s performance. Contact us to see how Hawkeye can help your team achieve optimal traffic distribution while reducing the operational burden of load balancer management.

How SRE teams are transforming video and voice quality management with AI

In today’s hybrid work environment, a 200ms delay in video conferencing isn’t just an inconvenience—it’s the difference between seamless collaboration and frustrated teams missing crucial conversations. For SRE teams managing real-time communications infrastructure, these quality issues create a perfect storm of complexity: they’re time-sensitive, impact-heavy, and notoriously difficult to diagnose.

The challenge isn’t just about watching network metrics. Modern video and voice applications generate massive amounts of telemetry data across multiple layers: network paths, codec behaviors, endpoint performance, and infrastructure health. When quality degrades, engineers face the daunting task of correlating data across these layers in real-time, often while users are actively reporting issues

The Hidden Complexity of Real-Time Traffic

Traditional network monitoring approaches fall short when dealing with real-time traffic issues. While your dashboard might show acceptable overall network performance, users still experience stuttering video and choppy audio. Why? Because real-time communications require a different class of network quality:

• Jitter and latency variations that barely impact web browsing can destroy video quality
• Packet loss patterns that standard monitoring might miss can create noticeable audio artifacts
• Micro-bursts of congestion can cause quality degradation without triggering traditional thresholds
• Quality metrics need to be analyzed end-to-end, across multiple network segments and providers

Adding to this complexity, modern video and voice applications dynamically adjust to network conditions, making it challenging to establish baseline performance metrics. An issue that causes severe quality problems in one session might have minimal impact in another, depending on codec adaptations and endpoint behaviors.

Enter Hawkeye: Your AI-Powered Network Quality Expert

Instead of manually correlating metrics across multiple tools and time ranges, imagine having an AI teammate that understands the nuanced relationships between network behavior and real-time traffic quality. Hawkeye transforms how teams handle these challenges:

1. Proactive Detection: By analyzing patterns across network layers, Hawkeye identifies potential quality issues before users report problems. It understands the specific network characteristics that impact real-time traffic and can spot subtle degradations traditional monitoring might miss.
2. Rapid Resolution: Instead of spending hours manually investigating, teams receive comprehensive analysis identifying the root cause and recommended actions. Hawkeye’s understanding of real-time traffic requirements means it can distinguish between general network issues and those specifically impacting video/voice quality.
3. Contextual Analysis: When quality issues occur, Hawkeye automatically correlates relevant data points:
   • Network path performance metrics
   • Infrastructure health indicators
   • Application-level quality metrics
   • Historical baseline comparisons
   • Configuration changes that might impact performance

The Transformed Workflow

The impact on daily operations is immediate. Traditional troubleshooting workflows require engineers to:

• Monitor multiple dashboards across network and application layers
• Manually correlate quality metrics with network performance
• Analyze historical trends to identify patterns
• Investigate potential infrastructure or configuration changes
• Coordinate with multiple teams to implement fixes

With Hawkeye, engineers start with a unified view that automatically brings together all relevant information. Routine issues are quickly resolved using recommended actions, while complex problems come with detailed investigation summaries that include data from across your environment.

Moving Beyond Reactive Monitoring

For organizations heavily dependent on real-time communications, the transformation Hawkeye brings extends beyond technical efficiency. It enables a fundamental shift from reactive quality management to proactive optimization:

• Quality issues are identified and resolved before they impact users
• Network capacity and configuration decisions are guided by AI-driven analysis
• Engineering teams focus on strategic improvements rather than firefighting
• Users experience consistently high-quality video and voice communications

Ready to Transform Your Real-Time Traffic Management?

Implementing Hawkeye alongside your existing tools is straightforward and begins paying dividends immediately. While this blog focuses on video and voice quality, Hawkeye’s flexible integration capabilities mean you can connect it to your entire observability stack, creating a unified intelligence layer across all your tools.

Take the next step toward transforming your real-time communications quality management. Contact us to see how Hawkeye can become your team’s AI-powered network quality expert.