Categories: Technical Deep Dive

How forward-thinking SRE teams are moving beyond alert automation

“Just tune your alert thresholds better.” “Set up more sophisticated routing rules.” “Create better runbooks.”

If you’re an SRE dealing with alert fatigue, you’ve heard all these suggestions before. Yet despite years of refinement, most teams still face a fundamental challenge: the volume and complexity of alerts continue to outpace our ability to handle them effectively. The reality is that traditional approaches to alert management are hitting their limits—not because they’re poorly implemented, but because they’re solving the wrong problem.

The issue isn’t just about routing alerts more efficiently or documenting better runbooks. It’s about the fundamental way we approach incident response. When a critical Splunk alert triggers a PagerDuty notification at 3 AM, the real problem isn’t the alert itself—it’s that a human has to wake up and spend precious time gathering context, analyzing logs, and determining the right course of action.

Beyond Alert Automation: The Current Reality

Today’s incident response stack is sophisticated. Splunk’s machine learning capabilities can detect anomalies in real-time, while PagerDuty’s intelligent routing ensures alerts reach the right people. Yet the reality in most enterprises is far more complex. Different teams often prefer different tools, leading to scenarios where application logs might live in Splunk, while cloud metrics flow to CloudWatch, and APM data resides in Datadog.

This fragmentation means that when an alert fires, engineers must:

1. Acknowledge the PagerDuty notification
2. Log into multiple systems
3. Write and refine Splunk queries
4. Correlate data across platforms
5. Document findings
6. Implement solutions

All while the clock is ticking and services might be degraded.

Enter Hawkeye: Reimagining Alert Response

Consider a fundamentally different approach. Instead of humans serving as the integration layer between tools, Hawkeye acts as an intelligent orchestrator that not only bridges Splunk and PagerDuty but can pull relevant information from your entire observability ecosystem. This isn’t about replacing any of your existing tools—it’s about having a GenAI powered SRE that maximizes their collective value and helps your team deliver results and scale.

Beyond Simple Integration

When a critical alert fires, Hawkeye springs into action before any human is notified. It automatically:

• Analyzes Splunk logs using sophisticated SPL queries
• Correlates patterns across different time periods
• Gathers context from other observability tools
• Prepares a comprehensive incident analysis
• Recommends specific actions based on historical success patterns

This happens in seconds, not the minutes or hours it would take a human engineer to manually perform these steps. More importantly, Hawkeye learns from each incident, continuously improving its ability to identify root causes and recommend effective solutions.

The Transformed Workflow

The transformation in daily operations is profound. Instead of starting their investigation from scratch when a PagerDuty alert comes in, engineers receive a complete context package from Hawkeye, including:

• Relevant log patterns identified in Splunk
• Historical context from similar incidents
• Correlation with other system metrics
• Specific recommendations for resolution

This shifts the engineer’s role from data gatherer to strategic problem solver, focusing their expertise where it matters most.

The Future of SRE Work: From Survival to Strategic Impact

The transformation Hawkeye brings to SRE teams extends far beyond technical efficiency. In today’s competitive landscape, where experienced SRE talent is both scarce and expensive, organizations face mounting pressure to maintain reliability while controlling costs. The traditional response—hiring more engineers—isn’t just expensive; it’s often not even possible given the limited talent pool.

Hawkeye fundamentally changes this equation. By automating routine investigations and providing intelligent analysis across your observability stack, it effectively multiplies the capacity of your existing team. This means you can handle growing system complexity without proportionally growing headcount. More importantly, it transforms the SRE role itself, addressing many of the factors that drive burnout and turnover:

• Engineers spend more time on intellectually engaging work like architectural improvements and capacity planning, rather than repetitive investigations. 
• The dreaded 3 AM wake-up calls become increasingly rare as Hawkeye handles routine issues autonomously (*roadmap, today it recommends an action plan). 
• New team members come up to speed faster, learning from Hawkeye’s accumulated knowledge base, and cross-training becomes easier as Hawkeye provides consistent, comprehensive investigation summaries.

For organizations, this translates directly to the bottom line through reduced recruitment costs, higher retention rates, and the ability to scale operations without scaling headcount. More subtly, it creates a virtuous cycle where happier, more engaged engineers deliver better systems, leading to fewer incidents and more time for innovation.

Real Impact, Real Results

Early adopters of this approach are seeing dramatic improvements:

• Reduction in mean time to resolution
• Fewer escalations to senior engineers
• More time for strategic initiatives
• Improved team morale and retention
• Better documentation and knowledge sharing

Getting Started

Implementing Hawkeye alongside your existing tools is a straightforward process that begins paying dividends immediately. While this blog focuses on Splunk and PagerDuty, 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

Ready to transform your fragmented toolchain into a unified, intelligent operations platform? Contact us to see how Hawkeye can become your team’s AI-powered SRE teammate and help your organization move from reactive to proactive operations.

How forward-thinking SRE teams are revolutionizing incident response with Hawkeye

Every minute counts in incident response. Yet studies show that SRE teams spend an average of 23 minutes just gathering context before they can begin meaningful problem-solving. For a team handling dozens of incidents per week, this translates to hundreds of hours spent just collecting data—time that could be spent on strategic improvements and innovation.

This reality persists despite having powerful tools like Datadog and PagerDuty at our disposal. These platforms excel at their core functions—Datadog providing deep observability and PagerDuty ensuring the right people are notified at the right time. Yet teams still struggle with response times and engineer burnout. The challenge isn’t with the tools themselves—it’s with how we use them. Adding to this challenge, most organizations have multiple observability tools, meaning engineers rarely have all the information they need in one place when that PagerDuty alert comes through.

The Current Landscape: Powerful Tools, Fragmented Response

Today’s incident management stack is more sophisticated than ever. PagerDuty orchestrates complex on-call schedules and escalation policies, while Datadog provides deep visibility into system behavior through real-time monitoring and alerting. Together, they form a powerful foundation for incident response.

Yet the reality in most enterprises is far more complex. Different teams often prefer different tools, leading to scenarios where application metrics might live in Datadog, while infrastructure logs reside in CloudWatch. When an alert fires, on-call engineers must navigate this fragmented landscape, often while half-awake and under pressure to resolve issues quickly.

Enter Hawkeye: Your Integration-Savvy GenAI Teammate

Consider a different approach. Instead of humans serving as the integration layer between tools, Hawkeye acts as an intelligent orchestrator that not only bridges Datadog and PagerDuty but can pull relevant information from your entire observability ecosystem. This isn’t about replacing any of your existing tools—it’s about having a GenAI powered SRE that maximizes their collective value and helps your team deliver results and scale.

Beyond Simple Integration

When a PagerDuty alert fires, Hawkeye springs into action before any human is notified. It automatically gathers context from across your observability stack, analyzing the situation and preparing a comprehensive response plan. This means that when an engineer does need to get involved, they’re not starting from zero—they’re starting with a complete understanding of the situation and clear next steps.

This multi-tool correlation happens in seconds, not the minutes or hours it would take a human engineer to manually gather and analyze data from each platform. More importantly, Hawkeye learns the relationships between different data sources, understanding which tools typically provide the most relevant information for specific types of incidents.

The Transformed Workflow

The transformation in daily operations is profound. Traditional workflows require engineers to wake up, log into multiple systems, gather context, and formulate a response plan—all while under the pressure of a live incident. Each context switch introduces delays and opportunities for oversight.

With Hawkeye, engineers instead start with a unified view of the issue and all the information needed to resolve it in one coherent root cause analysis. Routine issues are easily resolved by implementing the recommended actions, while complex problems come with detailed investigation summaries that already include relevant data from across your observability stack. This shifts the engineer’s role from data gatherer to strategic problem solver.

The Future of SRE Work: From Survival to Strategic Impact

The transformation Hawkeye brings to SRE teams extends far beyond technical efficiency. In today’s competitive landscape, where experienced SRE talent is both scarce and expensive, organizations face mounting pressure to maintain reliability while controlling costs. The traditional response—hiring more engineers—isn’t just expensive; it’s often not even possible given the limited talent pool.

Hawkeye fundamentally changes this equation. By automating routine investigations and providing intelligent analysis across your observability stack, it effectively multiplies the capacity of your existing team. This means you can handle growing system complexity without proportionally growing headcount. More importantly, it transforms the SRE role itself, addressing many of the factors that drive burnout and turnover:

• Engineers spend more time on intellectually engaging work like architectural improvements and capacity planning, rather than repetitive investigations. 
• The dreaded 3 AM wake-up calls become increasingly rare as Hawkeye handles routine issues autonomously (*roadmap, today it recommends an action plan). 
• New team members come up to speed faster, learning from Hawkeye’s accumulated knowledge base, and cross-training becomes easier as Hawkeye provides consistent, comprehensive investigation summaries.

For organizations, this translates directly to the bottom line through reduced recruitment costs, higher retention rates, and the ability to scale operations without scaling headcount. More subtly, it creates a virtuous cycle where happier, more engaged engineers deliver better systems, leading to fewer incidents and more time for innovation.

Getting Started

Implementing Hawkeye alongside your existing tools is a straightforward process that begins paying dividends immediately. While this blog focuses on Datadog and PagerDuty, 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

Ready to transform your fragmented toolchain into a unified, intelligent operations platform? Contact us to see how Hawkeye can become your team’s AI-powered SRE teammate and help your organization move from reactive to proactive operations.

 

How SRE teams are automating memory leak detection and prevention with Hawkeye

The PagerDuty alert breaks your concentration: “Average pod_memory_utilization_over_pod_limit GreaterThanOrEqualToThreshold 70.0” in the ‘frontend’ namespace. Your web application is gradually consuming more memory, and despite having comprehensive metrics and logs, pinpointing the root cause feels like trying to find a leak in a dark room where you can only see snapshots of where the water has been.

The Modern Memory Investigation Reality

In today’s Kubernetes environments, memory issues occur within the context of sophisticated observability stacks. CloudWatch captures container metrics, Prometheus tracks detailed memory stats, your APM solution monitors heap usage, and your logging platform records every OOMKilled event. Yet when memory leaks occur, this abundance of data often makes the investigation more complex rather than simpler.

A typical troubleshooting session involves juggling multiple tools and contexts:

You start in CloudWatch Container Insights, examining memory utilization trends. The metrics show a clear upward trend, but what’s driving it? Switching to Prometheus, you dive into more granular pod-level metrics, trying to correlate memory growth with specific activities or timeframes, you find increasing heap usage in several JVM instances, but is it normal application behavior or a genuine leak?

The investigation deepens as you cross-reference data:

• Container metrics show memory usage approaching limits
• JVM heap dumps indicate multiple suspected memory leaks
• Application logs reveal increased activity in certain components
• Kubernetes events show periodic OOMKilled pod terminations
• Request tracing shows certain API endpoints correlating with memory spikes

Each tool provides valuable data, but understanding how these pieces fit together requires constantly switching contexts and mentally correlating events across different timescales and granularities.

Why Memory Leaks Challenge Traditional Analysis

What makes memory leak investigation particularly demanding isn’t just identifying high memory usage – it’s understanding the pattern and root cause across your entire application stack. Memory issues often manifest in complex ways:

A memory leak in one microservice might only become apparent under specific traffic patterns. Garbage collection behavior can mask the true growth rate until it suddenly can’t keep up. Memory pressure on one node can cause pods to be evicted, triggering a cascade of rescheduling that spreads the impact across your cluster.

Your observability tools faithfully capture all these metrics and events, but understanding the sequence and cause-effect relationships requires simultaneously analyzing multiple data streams while understanding application behavior, container runtime characteristics, and Kubernetes resource management.

Hawkeye: Your Memory Analysis Expert

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to monitor memory usage – it’s how it analyzes memory patterns across multiple observability systems simultaneously. While an SRE would need to manually correlate data between container metrics, JVM heap dumps, application logs, and Kubernetes events, Hawkeye processes all these data streams in parallel to quickly identify patterns and anomalies.

This parallel analysis capability allows Hawkeye to discover cause-and-effect relationships that might take hours or days for humans to uncover. By simultaneously examining application behavior, container metrics, and system events, Hawkeye can trace how a memory leak in one component ripples through your entire system.

Real World Impact

For teams using Hawkeye, the transformation goes beyond faster leak detection. Engineers report a fundamental shift in how they approach memory management:

Instead of spending hours correlating data across different monitoring tools during incidents, they can focus on implementing systematic improvements based on Hawkeye’s comprehensive analysis. The mean time to resolution for memory-related incidents has decreased dramatically, but more importantly, teams can prevent many memory leaks entirely by acting on Hawkeye’s early warnings and recommendations.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of OOMKilled analysis while your team focuses on innovation.

Follow
Hawkeye

How VDI teams are revolutionizing their operations with AI-powered analysis

Picture this: It’s Monday morning, and your team is faced with dozens of ControlUp alerts about poor user experience scores across multiple virtual desktop sessions. Your VDI administrators are diving into metrics, comparing performance data, and trying to determine if this is a host-level issue, a network problem, or something else entirely. Meanwhile, end users are reporting sluggish performance, and productivity is taking a hit.

This scenario plays out in enterprises worldwide, where VDI teams struggle to maintain optimal desktop performance while managing increasingly complex virtual environments. ControlUp provides deep visibility into virtual desktop infrastructure, but as environments scale, the sheer volume of telemetry data can become overwhelming for human operators to process effectively.

The VDI Monitoring Challenge

Today’s virtual desktop environments are more complex than ever. With hybrid work becoming the norm, organizations are supporting unprecedented numbers of remote users, each requiring consistent, high-quality desktop experiences. ControlUp captures this complexity with detailed metrics across multiple layers:

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

While ControlUp excels at collecting and presenting this data, VDI teams often find themselves switching between different views, manually correlating metrics, and spending precious time piecing together the story behind performance issues. The challenge isn’t a lack of data—it’s making sense of it all at scale.

Enter Hawkeye: Your GenAI-Powered VDI Analyst

Imagine a fundamentally different approach to VDI operations. Instead of humans trying to process this flood of information, Hawkeye acts as an intelligent agent that understands the complex relationships in your virtual desktop environment. By integrating directly with ControlUp, Hawkeye transforms how teams monitor, analyze, and optimize their VDI infrastructure.

Beyond Traditional VDI Monitoring

When investigating a VDI incident, Hawkeye’s capabilities extend far beyond basic metric analysis:

• It understands the relationships between hosts, sessions, and applications
• It correlates performance metrics across different layers of the infrastructure
• It recognizes patterns in user behavior and resource consumption
• It identifies the impact of infrastructure changes on user experience
• It spots potential optimization opportunities before they affect users
• It learns from each investigation, building a deep understanding of your specific VDI environment

This analysis happens in seconds, not the minutes or hours it would take a human administrator to gather and process the same information.

The Transformed VDI Workflow

The transformation in daily operations is profound. Traditional workflows require VDI administrators to:

• Monitor multiple ControlUp dashboards
• Switch between different metric views
• Manually correlate performance data
• Document findings and actions taken
• Track down related changes and configurations

With Hawkeye, administrators instead start with a unified view of the issue and all relevant information needed to resolve it. Routine issues come with clear, actionable recommendations, while complex problems include detailed investigation summaries that already factor in data from across your VDI environment.

From Reactive to Proactive VDI Management

The integration of Hawkeye with ControlUp extends far beyond technical efficiency. In today’s competitive landscape, where experienced VDI administrators are both scarce and expensive, organizations face mounting pressure to maintain desktop performance while controlling costs.

Hawkeye fundamentally changes this equation 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
• Enabling faster onboarding of new team members

For organizations, this translates directly to improved end-user productivity, reduced support costs, and more strategic use of VDI expertise.

The Path Forward

As Hawkeye learns your VDI environment, it moves beyond reactive incident response to proactive optimization:

• 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

Implementing Hawkeye alongside ControlUp is designed to be straightforward, with immediate benefits:

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

Transform Your VDI Operations

Ready to revolutionize how you manage your virtual desktop infrastructure? Contact us to learn how Hawkeye can become your team’s AI-powered VDI analyst and help your organization tackle the complexity of modern virtual desktop environments.

Experience the future of VDI operations—where intelligent automation meets human expertise, and your team can focus on innovation rather than firefighting.

Follow
Hawkeye

How SRE teams are automating container deployment investigations with Hawkeye

Your team just deployed a new feature to production when PagerDuty alerts: “Maximum pod_container_status_waiting_reason_image_pull_error GreaterThanThreshold 0.0”. What should have been a routine deployment has turned into a complex investigation spanning multiple AWS services, container registries, and Kubernetes components.

The Modern Image Pull Investigation

Today’s container deployment issues occur in environments with sophisticated observability stacks. CloudWatch diligently logs every container event, Prometheus tracks your deployment metrics, and your CI/CD pipeline maintains detailed records of every build and deployment. Yet when image pull errors occur, this wealth of information often adds complexity to the investigation rather than simplifying it.

A typical troubleshooting session starts in your Kubernetes dashboard or CLI, where you see the ImagePullBackOff status. CloudWatch logs show the pull attempt failures, but the error messages can be frustratingly vague – “unauthorized” or “not found” don’t tell the whole story. You begin a methodical investigation across multiple systems:

First, you check AWS ECR to verify the image exists and its tags are correct. The image is there, but is it the version you expect? You dive into your CI/CD logs to confirm the build and push completed successfully. The pipeline logs show a successful push, but to which repository and with what permissions?

You switch to IAM to review the node’s instance role and its ECR policies. Everything looks correct, but when did these credentials last rotate? Back to CloudWatch to check the credential expiration timestamps. Meanwhile, you need to verify the Kubernetes service account configurations and secret mappings.

Each system provides critical pieces of the puzzle, but connecting them requires constant context switching and mental correlation of timestamps, configurations, and events across multiple AWS services and Kubernetes components.

Why Image Pull Errors Defy Quick Analysis

The complexity of modern container deployment means that image pull errors rarely have a single, obvious cause. Instead, they often result from subtle interactions between multiple systems:

An ECR authentication token might be valid, but the underlying instance role could be missing permissions. The Kubernetes secrets might be correctly configured, but the node might be pulling from the wrong registry endpoint. Network security groups and VPC endpoints add another layer of potential complications.

Your observability tools capture the symptoms across all these systems, but understanding the sequence of events and identifying the root cause requires simultaneously analyzing multiple authentication flows, networking paths, and permission boundaries.

Hawkeye: Your Deployment Detective

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to check permissions or validate configurations – it’s how it analyzes the complex interactions between AWS services, Kubernetes components, and your deployment pipeline simultaneously. While an SRE would need to manually switch between ECR, IAM, CloudWatch, and Kubernetes tooling to piece together the authentication flow, Hawkeye processes all these systems in parallel to quickly identify where the chain breaks down.

This parallel analysis capability allows Hawkeye to uncover cause-and-effect relationships that might take hours for humans to discover. By simultaneously examining IAM policies, ECR authentication flows, network configurations, and Kubernetes events, Hawkeye can trace how a seemingly minor infrastructure change can cascade into widespread deployment failures.

Real World Impact

For teams using Hawkeye, the transformation extends beyond faster resolution of image pull errors. Engineers report a fundamental shift in how they approach container deployment reliability:

Instead of spending hours jumping between different AWS consoles and Kubernetes tools during incidents, they can focus on implementing systematic improvements based on Hawkeye’s comprehensive analysis. The mean time to resolution for image pull failures has dropped dramatically, but more importantly, teams can prevent many issues entirely by acting on Hawkeye’s proactive recommendations for authentication and permission management.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of Image Pull Error analysis while your team focuses on innovation.

Follow
Hawkeye

Modern IT operations generate an overwhelming amount of telemetry data across dozens of tools and platforms. While traditional approaches struggle to process this complexity, Hawkeye takes a fundamentally different approach – using GenAI to transform how we analyze and respond to IT incidents. Let’s look under the hood to understand how Hawkeye works and why our security-first architecture sets us apart.

The Foundation: Security and Privacy Built in

Before diving into Hawkeye’s technical architecture, it’s crucial to understand our foundational security principles:

• Zero data storage: Hawkeye operates as a completely ephemeral platform. We process your telemetry data in real-time and never store historical information. Once an analysis session ends, all data is automatically purged from memory.
• Read-Only by default: Every connection to your infrastructure uses strictly read-only permissions. This isn’t just a policy – it’s architecturally enforced, making it technically impossible for Hawkeye to modify your systems or data.
• Costumer-controlled access: You maintain complete control through customer-specific external IDs and custom trust policies. Access can be revoked instantly at any time.

The Architecture: Step by Step

Let’s walk through how Hawkeye processes an incident or investigation, following our architectural diagram below:

Diag. Hawkeye from Neubird architecture step by step

Step 1. Finding the Right Approach Using Runbooks

When an incident occurs Hawkeye first step is selecting the appropriate analysis strategy. Using your private ChromaDB vector database, Hawkeye identifies similar historical patterns and successful investigation approaches. It uses an embedding of your issue and ChromaDB’s fast similarity search – without ever storing any of your telemetry data. 

As it learns more about your investigations this vector database can build up knowledge about investigation plans that work best for your systems. 

2. Creating the Investigation Plan Using LLMs Reasoning Capabilities

At this step, the LLM’s reasoning capabilities are leveraged to formulate a dynamic investigation plan, one that may be inspired by the retrieved information from step one but leverages the generative power of the LLM to adapt it. Hawkeye constructs a detailed chain of thought for the investigation, adapting its approach based on:

• The type of incident or investigation
• Available telemetry sources described through metadata
• Description of your architecture based on available information
• Historical patterns of similar issues

No configuration or telemetry data is included in the prompts to the LLMs. The choice of LLM changes based on the up to date benchmarks to achieve the best results.

Step 3. Telemetry Program Generation

Here’s where Hawkeye’s innovation shines. Instead of sending your sensitive telemetry data to an LLM, Hawkeye:

• Creates a specialized telemetry retrieval program
• Uses our fine-tuned LLM only for program logic, never for data processing
• Ensures all actual data handling happens in isolated memory space

The fine-tuning of the LLM (currently based on Llama 3.2 70B) is done by Neubird using only synthetic data programs and leveraging LARK files to control and validate the syntax of the generated telemetry program is valid and will produce results. 

Step 4. Secure Data Processing

Hawkeye executes the telemetry program in a secure, ephemeral runtime environment:

• Correlates data across multiple sources
• Performs necessary calculations and mathematical analysis in python
• Maintains strict memory isolation for each customer’s telemetry data
• Automatically purges all data after processing an investigation

Step 5. Real-Time Data Access Layer

Hawkeye’s second secret weapon is its secure data access layer. Queries to access data are all written in a common syntax which the fine-tuned LLM can generate with 100% accuracy, resulting in reliable and precise data access no matter what the data source is. Our secure data access layer:

• Uses temporary credentials with minimal scope
• Implements read-only access across all integrations
• Supports major cloud providers (AWS, Azure, GCP) and observability tools
• Never stores your telemetry data on disk
• Leverages schema on read technology, avoiding issues with schema drift

Step 6. Continuous Refinement

As the investigation progresses, Hawkeye:

• Iteratively refines its analysis approach based assertion on the data performed by the secure data processor, allowing to adapt to new information without sending telemetry data to the LLM
• Maintains audit trails of its reasoning and investigation steps
• Never uses your data to train or improve its models

Step 7. Final Analysis and Actions

Once all facts are available and the investigation has converged Hawkeye will produce:

• Detailed root cause analysis
• Clear evidence for all findings
• Specific recommended actions

In order to protect your telemetry and configuration data, Hawkeye leverages a privately hosted open source LLM to produce this final analysis.

Getting Started

Ready to transform your IT operations with Hawkeye? Here’s how to begin.

Conclusion

Hawkeye represents a fundamental shift in IT operations – combining the power of GenAI with unwavering commitment to security and privacy. By processing complex telemetry data in real-time while maintaining zero data persistence, we’re transforming how teams handle incidents and investigations.

Ready to see Hawkeye in action? Contact us to schedule a demo and learn how we can help transform your IT operations while maintaining the highest security standards.

Follow Hawkeye

How SRE teams are eliminating scaling headaches with Hawkeye

It’s peak holiday shopping season, and your e-commerce platform is experiencing record traffic. Your team initiates a scaling operation to handle the load, increasing the UI deployment’s replica count. But instead of scaling smoothly, pods remain stuck in pending state. The PagerDuty alert sounds: “Maximum pod_status_pending GreaterThanThreshold 0.0”. What should be a routine scaling operation has become a critical incident requiring deep investigation across multiple layers of your Kubernetes infrastructure.

The Modern Scaling Investigation Reality

In today’s Kubernetes environments, scaling issues occur within sophisticated observability stacks. CloudWatch captures detailed node and pod metrics while recording scheduler decisions. Prometheus tracks resource utilization, and your APM solution monitors service performance. Yet when scaling problems arise, this wealth of information often complicates rather than simplifies the investigation.

A typical troubleshooting session spans multiple systems and contexts:

You start in Prometheus, examining node capacity metrics. Resources seem available at the cluster level, but are they accessible to your workload? Switching to CloudWatch Container Insights, you dive into pod-level metrics, trying to understand resource utilization patterns. Your logging platform shows scheduler events, but the messages about resource pressure don’t align with your metrics.

The investigation expands as you correlate data across systems:

• Node metrics show available capacity
• Pod events indicate resource constraints
• Scheduler logs mention taint conflicts
• Prometheus alerts show resource quotas approaching limits
• Service mesh metrics indicate traffic distribution issues

Each tool provides critical information, but understanding how these pieces fit together requires constantly switching contexts and mentally correlating events across different abstraction layers in your Kubernetes stack.

Why Scaling Challenges Defy Quick Analysis

What makes scaling investigation particularly demanding isn’t just checking resource availability – it’s understanding the complex interaction between different layers of Kubernetes resource management and constraints:

Available CPU and memory might look sufficient at the cluster level, but pod anti-affinity rules could prevent optimal placement. Node selectors and taints might restrict where pods can run. Resource quotas at the namespace level might block scaling even when node capacity is available. Quality of Service classes affect pod scheduling priority, and Pod Disruption Budgets influence how workloads can be redistributed.

Your observability tools faithfully record all these metrics and events, but understanding the sequence and cause-effect relationships requires simultaneously analyzing multiple data streams while understanding Kubernetes scheduling logic, resource management, and workload distribution patterns.

Hawkeye: Your Scaling Expert

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to check resource metrics – it’s how it analyzes capacity constraints across multiple layers of your Kubernetes infrastructure simultaneously. While an SRE would need to manually correlate data between node metrics, scheduler logs, pod events, and cluster configurations, Hawkeye processes all these data streams in parallel to quickly identify bottlenecks and constraints.

This parallel analysis capability allows Hawkeye to discover cause-and-effect relationships that might take hours for humans to uncover. By simultaneously examining node capacity, scheduling rules, workload distribution patterns, and historical scaling behavior, Hawkeye can identify subtle constraints that wouldn’t be apparent from any single metric or log stream.

Real World Impact

For teams using Hawkeye, the transformation goes beyond faster scaling incident resolution. Engineers report a fundamental shift in how they approach capacity management:

Instead of spending hours correlating data across different monitoring tools during scaling incidents, they can focus on implementing systematic improvements based on Hawkeye’s comprehensive analysis. The mean time to resolution for scaling-related incidents has decreased dramatically, but more importantly, teams can prevent many scaling bottlenecks entirely by acting on Hawkeye’s early warnings and recommendations.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of pod_status_pending analysis while your team focuses on innovation.

Follow Hawkeye

How Desktop Infrastructure Teams are Conquering the Morning Login Storm

It’s 8:45 AM, and your phone lights up with a flood of tickets. “VDI is crawling,” reads the first message. “Can’t access my desktop,” says another. Within minutes, your ServiceNow queue is filled with frustrated users reporting login times stretching past three minutes. You’re facing the dreaded “morning login storm,” and somewhere in the maze of profiles, network traffic, and host metrics lies the root cause – if only you could find it fast enough.

For desktop infrastructure teams, this scenario is all too familiar. In a recent case study, a Fortune 500 company reported that their average login times had ballooned from 45 seconds to over 180 seconds, affecting 65% of their workforce. The business impact? Thousands of lost productivity hours and mounting frustration from both users and IT staff.

The Complex Reality of Modern VDI Environments

Today’s VDI deployments are far more complex than their predecessors. Consider the interconnected components that must work in perfect harmony for a single login:

• Profile services managing user data
• Network infrastructure handling massive morning traffic
• Host resources balancing compute demands
• Storage systems managing IO queues
• Authentication services processing credentials

Traditional monitoring approaches often fall short because they focus on individual metrics rather than the holistic user experience. Your dashboard might show green status lights while users face unacceptable delays. More concerning, by the time you’ve collected and correlated data from multiple tools, precious troubleshooting time has been lost.

The Hidden Costs of Slow Logins

The impact of VDI performance issues extends far beyond the obvious frustration of waiting for a desktop to load. Organizations face:

• Lost productivity during peak business hours
• Increased support ticket volume overwhelming IT teams
• Shadow IT as users seek alternatives
• Employee satisfaction and retention challenges
• Reduced confidence in IT infrastructure

One desktop administrator we spoke with put it perfectly: “Every minute of login delay is multiplied by hundreds of users. It’s not just time we’re losing – it’s trust.”

Enter Hawkeye: Your AI-Powered VDI Performance Partner

This is where a fundamentally different approach comes into play. Instead of relying on static thresholds and manual correlation, Hawkeye acts as an intelligent teammate that understands the complex interplay of VDI components.

In a recent deployment, Hawkeye identified a perfect storm of conditions causing login delays:

• Profile load times exceeding 90 seconds for 75% of sessions
• 40% packet retransmission rates during peak periods
• Profile server CPU utilization spiking to 92%
• Storage latency averaging 45ms for read operations
• Cache hit ratios dropping to 35% during login storms

More importantly, Hawkeye didn’t just collect these metrics – it understood their relationships and impact on the user experience. Within minutes, it provided a comprehensive analysis and actionable remediation steps.

The Transformed Workflow

With Hawkeye as part of the team, the VDI support workflow changes dramatically:

Before Hawkeye:

• Manual correlation of multiple monitoring tools
• Hours spent gathering data from various sources
• Reactive response to user complaints
• Trial-and-error troubleshooting

With Hawkeye:

• Instant correlation of performance metrics
• Proactive identification of emerging issues
• Clear visualization of impact and root cause
• Specific, prioritized remediation steps

Real Results in Real Environments

Organizations leveraging Hawkeye for VDI performance management are seeing transformative results:

• Login times reduced by up to 75%
• Support ticket volume decreased by 60%
• Mean time to resolution cut from hours to minutes
• Proactive resolution of 40% of potential issues before user impact

Looking Forward: From Reactive to Proactive

The future of VDI management isn’t about adding more monitoring tools or building more complex dashboards. It’s about having an intelligent teammate that understands the intricacies of your environment and can take action before users are impacted.

Hawkeye is leading this transformation by:

• Learning normal login patterns for your environment
• Predicting potential bottlenecks before they impact users
• Automatically correlating events across your VDI stack
• Providing clear, actionable recommendations for optimization

Ready to Transform Your VDI Operations?

If you’re ready to move beyond the limitations of traditional monitoring and embrace the future of intelligent VDI management, we’re here to help. Contact us to learn how Hawkeye can become your team’s AI-powered desktop infrastructure expert and help deliver the consistent, high-performance VDI experience your users deserve.

Follow
Hawkeye

How SRE teams are revolutionizing virtualization operations with GenAI

It’s 2 AM, and your phone lights up with another alert: “Critical: Database VM Performance Degradation.” As you dive into your KubeVirt dashboard, you’re faced with a wall of metrics – CPU throttling, IO wait times, memory pressure, and storage latency all competing for your attention. Which metric matters most? What’s the root cause? And most importantly, how quickly can you restore service before it impacts your business?

For SRE teams managing virtualized workloads on Kubernetes, this scenario is all too familiar. KubeVirt has revolutionized how we run virtual machines on Kubernetes, but it’s also introduced new layers of complexity in performance monitoring and troubleshooting. When a VM starts degrading, engineers must correlate data across multiple layers: the VM itself, the KubeVirt control plane, the underlying Kubernetes infrastructure, and the physical hardware – all while under pressure to resolve the issue quickly.

The Reality of KubeVirt Performance Investigation

Traditional approaches to VM performance troubleshooting often fall short in Kubernetes environments. Consider a recent incident at a major financial services company: Their production database VM suddenly showed signs of performance degradation. The traditional investigation process looked something like this:

1. Check VM metrics in KubeVirt dashboard
2. Review node resource utilization
3. Analyze storage metrics
4. Investigate guest OS metrics
5. Check impact on dependent services
6. Correlate timestamps across different metric sources
7. Draw conclusions from fragmented data

This manual process typically takes hours, requires multiple context switches between tools, and often misses crucial correlations that could lead to faster resolution. Meanwhile, dependent services degrade, and business impact compounds by the minute.

The Hidden Costs of Manual Investigation

The true cost of traditional VM performance troubleshooting extends far beyond just the immediate incident:

• Engineering Time: Senior engineers spend hours manually correlating data across different layers of the stack
• Business Impact: Extended resolution times mean longer service degradation
• Team Burnout: Complex investigations at odd hours contribute to SRE team fatigue
• Missed Patterns: Without systematic analysis, recurring patterns often go unnoticed
• Knowledge Gap: Detailed investigation steps often remain undocumented, making knowledge transfer difficult

Enter Hawkeye: Your AI-Powered VM Performance Expert

Hawkeye transforms this investigation process through its unique ability to simultaneously analyze and correlate data across your entire stack. Let’s look at how Hawkeye handled the same database VM performance incident:

Within minutes of the initial alert, Hawkeye had:

• Identified CPU throttling at 98% of allocated limits
• Correlated high IO wait times (45ms) with storage IOPS throttling
• Detected memory pressure despite adequate allocation
• Quantified the impact on dependent services (35% increased latency)
• Generated a comprehensive analysis with actionable recommendations

But Hawkeye’s value goes beyond just speed. Its ability to understand the complex relationships between different layers of your infrastructure means it can identify root causes that might be missed in manual investigation. In this case, Hawkeye correlated the VM’s performance degradation with recent storage class QoS limits and memory balloon device behavior – connections that might take hours to discover manually.

The Transformed Workflow

With Hawkeye as part of your team, the investigation workflow changes dramatically:

1. Instant Context: Instead of jumping between dashboards, engineers start with a complete picture of the incident
2. Automated Correlation: Hawkeye automatically connects metrics across VM, host, storage, and service mesh layers
3. Clear Action Items: Each analysis includes specific, prioritized recommendations for resolution
4. Continuous Learning: Hawkeye builds a knowledge base of your environment, improving its analysis over time

Moving from Reactive to Proactive

The real power of Hawkeye lies in its ability to help teams shift from reactive troubleshooting to proactive optimization. By continuously analyzing your environment, Hawkeye can:

• Identify potential resource constraints before they cause incidents
• Recommend optimal VM resource allocations based on actual usage patterns
• Alert on subtle performance degradation patterns before they become critical
• Provide trend analysis to support capacity planning decisions

Getting Started with Hawkeye

Transforming your KubeVirt operations with Hawkeye is straightforward:

1. Connect your telemetry sources:
   • KubeVirt metrics
   • Kubernetes cluster metrics
   • Storage performance data
   • Service mesh telemetry
2. Configure your preferred incident management integration
3. Start receiving AI-powered insights immediately

The Future of VM Operations

As virtualization continues to evolve with technologies like KubeVirt, the old ways of monitoring and troubleshooting no longer suffice. Hawkeye represents a fundamental shift from manual correlation to AI-driven analysis, transforming how SRE teams manage virtual infrastructure and enabling them to focus on strategic improvements rather than reactive firefighting.

Ready to transform your KubeVirt operations? Contact us to see how Hawkeye can become your team’s AI-powered SRE teammate and help your organization tackle the complexity of modern virtualization environments.

Follow
Hawkeye

How SRE teams are revolutionizing application debugging with Hawkeye

The PagerDuty alert comes in at the worst possible time: “Maximum pod_container_status_waiting_reason_crash_loop_back_off GreaterThanThreshold 0.0”. Your application is caught in the dreaded CrashLoopBackOff state. While your CloudWatch logs capture every crash and restart, the sheer volume of error data makes finding the root cause feel like solving a puzzle in the dark.

The Traditional Debug Dance

In a modern Kubernetes environment, SREs have powerful tools at their disposal. CloudWatch diligently captures every log line, metrics flow into Prometheus, and your APM solution tracks every transaction. Yet, when faced with a CrashLoopBackOff, these tools often present more questions than answers.

A typical investigation starts with CloudWatch Logs, where you’re immediately confronted with thousands of entries across multiple restart cycles. You begin the methodical process of piecing together the story: the first crash occurrence, any changes in error messages between restarts, and potential patterns in the pod’s behavior before each failure.

Next comes the metrics investigation in Prometheus. You pull up graphs of memory usage, CPU utilization, and network activity, looking for correlations with the crash timing. Everything looks normal, which is both reassuring and frustrating – no obvious resource constraints to blame.

Then it’s time to dig deeper. You pull up the Kubernetes events, checking for any cluster-level issues that might be affecting the pod. You review recent deployments in your CI/CD pipeline, wondering if a configuration change slipped through code review. Each step adds more data but doesn’t necessarily bring you closer to a resolution.

Why CrashLoopBackOff Defies Traditional Analysis

What makes CrashLoopBackOff particularly challenging isn’t a lack of data – it’s the complexity of piecing together the right narrative from overwhelming amounts of information. Modern observability tools give us unprecedented visibility into our systems, but they don’t inherently understand the relationships between different signals.

A single CrashLoopBackOff incident typically spans multiple dimensions:

The application layer might show clean logs right up until the crash, missing the crucial moments that would explain the failure. System metrics might appear normal because the pod isn’t running long enough to establish baseline behavior. Kubernetes events capture the restarts but not the underlying cause.

Even more challenging is the ripple effect through your microservices architecture. A crashing service can trigger retry storms from dependent services, creating noise that obscures the original problem. Your observability tools faithfully record every detail, but understanding the cascade of events requires deep system knowledge and careful analysis.

Hawkeye: Bringing Context to Chaos

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to process logs faster than humans – it’s how it understands the complex relationships between different parts of your system. When Hawkeye analyzes a CrashLoopBackOff, it doesn’t just look at the logs in isolation. It builds a comprehensive narrative by:

Simultaneously analyzing data across multiple observability systems and environments. While humans must context-switch between different tools and mentally piece together timelines, Hawkeye can instantly correlate events across your entire observability stack. What might take an SRE hours of checking CloudWatch logs, then Prometheus metrics, then deployment histories, and then trying to build a coherent timeline, Hawkeye can process in seconds by analyzing all these data sources in parallel.

Analyzing the impact on your entire service mesh. Instead of just focusing on the crashing pod, Hawkeye maps out how the failure ripples through your system, helping identify whether the crash is a cause or symptom of a broader issue.

Correlating deployment changes with system behavior. Hawkeye doesn’t just know what changed – it understands how those changes interact with your existing infrastructure and configuration.

Real World Impact

For teams that have integrated Hawkeye into their operations, the transformation goes beyond faster resolution times. Engineers report a fundamental shift in how they approach application reliability:

Instead of spending hours reconstructing what happened during an incident, they can focus on implementing Hawkeye’s targeted recommendations for system improvement. The mean time to resolution for CrashLoopBackOff incidents has dropped from hours to minutes, but more importantly, repeat incidents have become increasingly rare as Hawkeye helps teams address root causes rather than symptoms.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of crash analysis while your team focuses on innovation.

Follow
Hawkeye